School of Technology, PUCRS, Avenida Ipiranga 6681, Porto Alegre, RS 90619-900, Brazil; e-mails: henry.cagnini@edu.pucrs.br, silvia.dores@acad.pucrs.br, rodrigo.barros@pucrs.br"/> Computing School, University of Kent, Giles Ln, Canterbury CT2 7NZ, UK; e-mail: a.a.freitas@kent.ac.uk"/>
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2023 Volume 38
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REVIEW   Open Access    

A survey of evolutionary algorithms for supervised ensemble learning

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  • Abstract: This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.
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  • Abadi , M., Agarwal , A., Barham , P., Brevdo , E., Chen , Z., Citro , C., Corrado , G. S., Davis , A., Dean , J., Devin , M., Ghemawat , S., Goodfellow , I., Harp , A., Irving , G., Isard , M., Jozefowicz , R., Jia , Y., Kaiser , L., Kudlur , M., Levenberg , J., Mané , D., Schuster , M., Monga , R., Moore , S., Murray , D., Olah , C., Shlens , J., Steiner , B., Sutskever , I., Talwar , K., Tucker , P., Vanhoucke , V., Vasudevan , V., Viégas , F., Vinyals , O., Warden , P., Wattenberg , M., Wicke , M., Yu , Y. & Zheng , X. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/.

    Google Scholar

    Adair , J., Brownlee , A., Daolio , F. & Ochoa , G. 2017. Evolving training sets for improved transfer learning in brain computer interfaces. In International Workshop on Machine Learning, Optimization, and Big Data, 186–197. Springer.

    Google Scholar

    Albukhanajer , W. A., Jin , Y. & Briffa , J. A. 2017. Classifier ensembles for image identification using multi-objective pareto features. Neurocomputing 238, 316–327.

    Google Scholar

    Ali , S. & Majid , A. 2015. Can–Evo–Ens: classifier stacking based evolutionary ensemble system for prediction of human breast cancer using Amino Acid sequences. Journal of Biomedical Informatics 54, 256–269.

    Google Scholar

    Aliakbarian , M. S. & Fanian , A. 2013. Internet traffic classification usqing MOEA and online refinement in voting on ensemble methods. In Iranian Conference on Electrical Engineering, 1–6. IEEE.

    Google Scholar

    Almeida , L. M. & Galvão , P. S. 2016. Ensembles with clustering-and-selection model using evolutionary algorithms. In Brazilian Conference on Intelligent Systems, 444–449. IEEE.

    Google Scholar

    Asafuddoula , M., Verma , B. & Zhang , M. 2017. A divide-and-conquer based ensemble classifier learning by means of many-objective optimization. IEEE Transactions on Evolutionary Computation 22(5), 762–777.

    Google Scholar

    Athar , A., Butt , W. H., Anwar , M. W. & Latif , M. 2017. Exploring the ensemble of classifiers for sentimental analysis: A systematic literature review. In International Conference on Machine Learning and Computing, 410–414. ACM.

    Google Scholar

    Augusto , D. A., Barbosa , H. J. C. & Ebecken , N. F. F. 2010. Coevolutionary multi-population genetic programming for data classification. In Conference on Genetic and Evolutionary Computation, 933–940. ACM.

    Google Scholar

    Bagheri , M. A., Gao , Q. & Escalera , S. 2013. A genetic-based subspace analysis method for improving error-correcting output coding. Pattern Recognition 46(10), 2830–2839.

    Google Scholar

    Basto-Fernandes , V., Yevseyeva , I., Méndez , J. R., Zhao , J., Fdez-Riverola , F. & Emmerich , M. T. M. 2016. A spam filtering multi-objective optimization study covering parsimony maximization and three-way classification. Applied Soft Computing 48, 111–123.

    Google Scholar

    Basto-Fernandes , V., Yevseyeva , I., Ruano-Ordás , D., Zhao , J., Fdez-Riverola , F., Méndez , J. R. & Emmerich , M. T. M. 2018. Quadcriteria optimization of binary classifiers: error rates, coverage, and complexity. In EVOLVE – A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI, Tantar , A.-A., Tantar , E., Emmerich , M., Legrand , P., Alboaie , L. & Luchian , H. (eds), 37–49. Springer.

    Google Scholar

    Batista , J. d. O., Rodrigues , R. B. & Varejão , F. M. 2017. Soft computing classifier ensemble for fault diagnosis. In International Symposium on Industrial Electronics, 1348–1353. IEEE.

    Google Scholar

    Bautista , M. Á., Pujol , O., Baró , X. & Escalera , S. 2011. Introducing the separability matrix for error correcting output codes coding. In International Workshop on Multiple Classifier Systems, 227–236. Springer.

    Google Scholar

    Bazi , Y., Alajlan , N., Melgani , F., AlHichri , H. & Yager , R. R. 2014. Robust estimation of water chlorophyll concentrations with Gaussian process regression and IOWA aggregation operators. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(7), 3019–3028.

    Google Scholar

    Bhowan , U., Johnston , M. & Zhang , M. 2011. Evolving ensembles in multi-objective genetic programming for classification with unbalanced data. In Conference on Genetic and Evolutionary Computation, 1331–1338. ACM.

    Google Scholar

    Bhowan , U., Johnston , M. & Zhang , M. 2013. Comparing ensemble learning approaches in genetic programming for classification with unbalanced data. In Conference on Genetic and Evolutionary Computation, 135–136. ACM.

    Google Scholar

    Breiman , L. 1996. Bagging predictors. Machine Learning 24(2), 123–140.

    Google Scholar

    Breiman , L. 2001. Random forests. Machine Learning 45(1), 5–32.

    Google Scholar

    Britto Jr., A. S., Sabourin , R. & Oliveira , L. E. S. 2014. Dynamic selection of classifiers – A comprehensive review. Pattern Recognition 47(11), 3665–3680.

    Google Scholar

    Cagnini , H., Basgalupp , M. & Barros , R. 2018. Increasing boosting effectiveness with estimation of distribution algorithms. In Congress on Evolutionary Computation, 1–8. IEEE.

    Google Scholar

    Cao , J.-J., Kwong , S., Wang , R. & Li , K. 2014. An indicator-based selection multi-objective evolutionary algorithm with preference for multi-class ensemble. In International Conference on Machine Learning and Cybernetics, 147–152. IEEE.

    Google Scholar

    Cao , P., Li , B., Zhao , D. & Zaiane , O. 2013a. A novel cost sensitive neural network ensemble for multiclass imbalance data learning. In International Joint Conference on Neural Networks, 1–8. IEEE.

    Google Scholar

    Cao , P., Zhao , D. & Zaiane , O. 2013b. Measure optimized cost-sensitive neural network ensemble for multiclass imbalance data learning. In International Conference on Hybrid Intelligent Systems, 35–40. IEEE.

    Google Scholar

    Castro , P. A. D. & Von Zuben , F. J. 2011. Learning ensembles of neural networks by means of a Bayesian artificial immune system. IEEE Transactions on Neural Networks 22(2), 304–316.

    Google Scholar

    Chaurasiya , R. K., Londhe , N. D. & Ghosh , S. 2016. Binary DE-based channel selection and weighted ensemble of SVM classification for novel brain–computer interface using Devanagari script-based P300 Speller Paradigm. International Journal of Human–Computer Interaction 32(11), 861–877.

    Google Scholar

    Chen , H. & Yao , X. 2006. Evolutionary multiobjective ensemble learning based on Bayesian feature selection. In Congress on Evolutionary Computation, 267–274. IEEE.

    Google Scholar

    Chen , W.-C., Tseng , L.-Y. & Wu , C.-S. 2014. A unified evolutionary training scheme for single and ensemble of feedforward neural network. Neurocomputing 143, 347–361.

    Google Scholar

    Chen , Y., Yang , B. & Abraham , A. 2007. Flexible neural trees ensemble for stock index modeling. Neurocomputing 70(4), 697–703.

    Google Scholar

    Chen , Y. & Zhao , Y. 2008. A novel ensemble of classifiers for microarray data classification. Applied Soft Computing 8(4), 1664–1669.

    Google Scholar

    Chiu , C.-Y. & Verma , B. 2013. Effect of varying hidden neurons and data size on clusters, layers, diversity and accuracy in neural ensemble classifier. In International Conference on Computational Science and Engineering, 455–459. IEEE.

    Google Scholar

    Chiu , C.-Y. & Verma , B. 2014. Multi-objective evolutionary algorithm based optimization of neural network ensemble classifier. In International Conference on Signal Processing and Communication Systems, 1–5. IEEE.

    Google Scholar

    Chyzhyk , D., Savio , A. & Graña , M. 2015. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Networks 68, 23–33.

    Google Scholar

    Coelho , A. L. V., Lima , C. A. M. & Von Zuben , F. J. 2003. GA-based selection of components for heterogeneous ensembles of support vector machines. In Congress on Evolutionary Computation, 2238–2245. IEEE.

    Google Scholar

    Connolly , J.-F., Granger , E. & Sabourin , R. 2011. Comparing dynamic PSO algorithms for adapting classifier ensembles in video-based face recognition. In Workshop on Computational Intelligence in Biometrics and Identity Management, 1–8. IEEE.

    Google Scholar

    Connolly , J.-F., Granger , E. & Sabourin , R. 2012. Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition. Pattern Recognition 45(7), 2460–2477.

    Google Scholar

    Connolly , J.-F., Granger , E. & Sabourin , R. 2013. Dynamic multi-objective evolution of classifier ensembles for video face recognition. Applied Soft Computing 13(6), 3149–3166.

    Google Scholar

    Cordón , O. & Trawiński , K. 2013. A novel framework to design fuzzy rule-based ensembles using diversity induction and evolutionary algorithms-based classifier selection and fusion. In International Work-Conference on Artificial Neural Networks, 36–58. Springer.

    Google Scholar

    Cruz , R. M. O., Sabourin , R. & Cavalcanti , G. D. C. 2018. Dynamic classifier selection: recent advances and perspectives. Information Fusion 41, 195–216.

    Google Scholar

    Das , A. K., Das , S. & Ghosh , A. 2017. Ensemble feature selection using bi-objective genetic algorithm. Knowledge-Based Systems 123, 116–127.

    Google Scholar

    Davidsen , S. A. & Padmavathamma , M. 2015. Multi-modal evolutionary ensemble classification in medical diagnosis problems. In International Conference on Advances in Computing, Communications and Informatics, 1366–1370. IEEE.

    Google Scholar

    De Lima , T. P. F. & Ludermir , T. B. 2013. Optimizing dynamic ensemble selection procedure by evolutionary extreme learning machines and a noise reduction filter. In International Conference on Tools with Artificial Intelligence, 546–552. IEEE.

    Google Scholar

    de Lima , T. P. F. & Ludermir , T. B. 2014. Ensembles of evolutionary extreme learning machines through differential evolution and fitness sharing. In International Joint Conference on Neural Networks, 2677–2682. IEEE.

    Google Scholar

    de Lima , T. P. F., Sergio , A. T. & Ludermir , T. B. 2014. Improving classifiers and regions of competence in dynamic ensemble selection. In Brazilian Conference on Intelligent Systems, 13–18. IEEE.

    Google Scholar

    de Sá , A. G. C., Pinto , W. J. G. S., Oliveira , L. O. V. B. & Pappa , G. L. 2017. RECIPE: A grammar-based framework for automatically evolving classification pipelines. In European Conference on Genetic Programming, 246–261. Springer.

    Google Scholar

    De Stefano , C., Cioppa , A. D. & Marcelli , A. 2013. Evolutionary approaches for pooling classifier ensembles: Performance evaluation. In International Conference of Soft Computing and Pattern Recognition, 309–314. IEEE.

    Google Scholar

    De Stefano , C., Folino , G., Fontanella , F. & Di Freca , A. S. 2014. Using Bayesian networks for selecting classifiers in GP ensembles. Information Sciences 258, 200–216.

    Google Scholar

    Deb , K. 2011. Multi-objective optimisation using evolutionary algorithms: an introduction. In Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, 3–34. Springer.

    Google Scholar

    Deb , K., Pratap , A., Agarwal , S. & Meyarivan , T. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2), 182–197.

    Google Scholar

    Debie , E., Shafi , K., Lokan , C. & Merrick , K. 2013a. Performance analysis of rough set ensemble of learning classifier systems with differential evolution based rule discovery. Evolutionary Intelligence 6(2), 109–126.

    Google Scholar

    Debie , E., Shafi , K., Merrick , K. & Lokan , C. 2016. On taxonomy and evaluation of feature selection-based learning classifier system ensemble approaches for data mining problems. Computational Intelligence 33(3), 554–578.

    Google Scholar

    Debie , E. S., Shafi , K. & Lokan , C. 2013b. REUCS-CRG: reduct based ensemble of supervised classifier system with combinatorial rule generation for data mining. In Conference on Genetic and Evolutionary Computation, 1251–1258. ACM.

    Google Scholar

    Dehuri , S., Jagadev , A. K. & Cho , S.-B. 2013. Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble. Procedia Computer Science 23, 84–95.

    Google Scholar

    Dietterich , T. G. 2000. Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems, 1–15. Springer.

    Google Scholar

    Dos Santos , E. M., Oliveira , L. S., Sabourin , R. & Maupin , P. 2008a. Overfitting in the selection of classifier ensembles: a comparative study between PSO and GA. In Conference on Genetic and Evolutionary Computation, 1423–1424. ACM.

    Google Scholar

    Dos Santos , E. M., Sabourin , R. & Maupin , P. 2008b. Pareto analysis for the selection of classifier ensembles. In Conference on Genetic and Evolutionary Computation, 681–688. ACM.

    Google Scholar

    Duell , P., Fermin , I. & Yao , X. 2006. Speciation techniques in evolved ensembles with negative correlation learning. In Congress on Evolutionary Computation, 3317–3321. IEEE.

    Google Scholar

    Dufourq , E. & Pillay , N. 2014. Hybridizing evolutionary algorithms for creating classifier ensembles. In World Congress on Nature and Biologically Inspired Computing, 84–90. IEEE.

    Google Scholar

    e Silva , E. J. d. R., Ludermir , T. B. & Almeida , L. M. 2013. Clustering and selection using grouping genetic algorithms for blockmodeling to construct neural network ensembles. In International Conference on Tools with Artificial Intelligence, 420–425. IEEE.

    Google Scholar

    Escalante , H. J., Acosta-Mendoza , N., Morales-Reyes , A. & Gago-Alonso , A. 2013. Genetic programming of heterogeneous ensembles for classification. In Iberoamerican Congress on Pattern Recognition, 9–16. Springer.

    Google Scholar

    Escovedo , T., da Cruz , A., Vellasco , M. & Koshiyama , A. 2013a. NEVE: a neuro-evolutionary ensemble for adaptive learning. In International Conference on Artificial Intelligence Applications and Innovations, 636–645. Springer.

    Google Scholar

    Escovedo , T., da Cruz , A. V. A., Vellasco , M. & Koshiyama , A. S. 2013b. Using ensembles for adaptive learning: a comparative approach. In International Joint Conference on Neural Networks, 1–7. IEEE.

    Google Scholar

    Escovedo , T., da Cruz , A. V. A., Vellasco , M. M. & Koshiyama , A. S. 2013c. Learning under concept drift using a neuro-evolutionary ensemble. International Journal of Computational Intelligence and Applications 12(4), 1340002.

    Google Scholar

    Escovedo , T., da Cruz , A. A., Koshiyama , A., Melo , R. & Vellasco , M. 2014. NEVE++: a neuro-evolutionary unlimited ensemble for adaptive learning. In International Joint Conference on Neural Networks, 3331–3338. IEEE.

    Google Scholar

    Fatima , I., Fahim , M., Lee , Y.-K. & Lee , S. 2013. Classifier ensemble optimization for human activity recognition in smart homes. In International Conference on Ubiquitous Information Management and Communication, 1–7. ACM.

    Google Scholar

    Fernández , A., del Ro , S. & Herrera , F. 2016a. A first approach in evolutionary fuzzy systems based on the lateral tuning of the linguistic labels for big data classification. In International Conference on Fuzzy Systems, 1437–1444. IEEE.

    Google Scholar

    Fernández , J. C., Cruz-Ramrez , M. & Hervás-Martnez , C. 2016b. Sensitivity versus accuracy in ensemble models of artificial neural networks from multi-objective evolutionary algorithms. Neural Computing and Applications 30(1), 289–305.

    Google Scholar

    Folino , G., Pisani , F. S. & Sabatino , P. 2016. An incremental ensemble evolved by using genetic programming to efficiently detect drifts in cyber security datasets. In Conference on Genetic and Evolutionary Computation, 1103–1110. ACM.

    Google Scholar

    Folino , G., Pizzuti , C. & Spezzano , G. 2006. Improving cooperative GP ensemble with clustering and pruning for pattern classification. In Conference on Genetic and Evolutionary Computation, 791–798. ACM.

    Google Scholar

    Folino , G., Pizzuti , C. & Spezzano , G. 2007a. An adaptive distributed ensemble approach to mine concept-drifting data streams. In International Conference on Tools with Artificial Intelligence, 183–188. IEEE.

    Google Scholar

    Folino , G., Pizzuti , C. & Spezzano , G. 2007b. StreamGP: tracking evolving GP ensembles in distributed data streams using fractal dimension. In Conference on Genetic and Evolutionary Computation, 1751–1751. ACM.

    Google Scholar

    Folino , G., Pizzuti , C. & Spezzano , G. 2010. An ensemble-based evolutionary framework for coping with distributed intrusion detection. Genetic Programming and Evolvable Machines 11 (2), 131–146.

    Google Scholar

    Freitas , A. A. 2004. A critical review of multi-objective optimization in data mining: a position paper. ACM SIGKDD Explorations Newsletter 6(2), 77–86.

    Google Scholar

    Freund , Y. & Schapire , R. E. 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, 23–37. Springer.

    Google Scholar

    Freund , Y. & Schapire , R. E. 1996. Experiments with a new boosting algorithm. In International Conference on Machine Learning, 148–156. International Machine Learning Society.

    Google Scholar

    Fuqiang , D., Minqing , Z. & Jia , L. 2014. Virus-evolutionary genetic algorithm based selective ensemble for steganalysis. In International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 553–558. IEEE.

    Google Scholar

    Galar , M., Fernández , A., Barrenechea , E. & Herrera , F. 2013. EUSBoost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition 46(12), 3460–3471.

    Google Scholar

    Galea , M., Shen , Q. & Levine , J. 2004. Evolutionary approaches to fuzzy modelling for classification. The Knowledge Engineering Review 19(1), 27–59.

    Google Scholar

    Garg , A. & Lam , J. S. L. 2015. Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach. Journal of Cleaner Production 102, 246–263.

    Google Scholar

    Gomes , H. M., Barddal , J. P., Enembreck , F. & Bifet , A. 2017. A survey on ensemble learning for data stream classification. ACM Computing Surveys 50(2), 23.

    Google Scholar

    Gu , S. & Jin , Y. 2014. Generating diverse and accurate classifier ensembles using multi-objective optimization. In Symposium on Computational Intelligence in Multi-Criteria Decision-Making, 9–15. IEEE.

    Google Scholar

    Hansen , L. K. & Salamon , P. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(10), 993–1001.

    Google Scholar

    Haque , M. N., Noman , M. N., Berretta , R. & Moscato , P. 2016. Optimising weights for heterogeneous ensemble of classifiers with differential evolution. In Congress on Evolutionary Computation, 233–240. IEEE.

    Google Scholar

    Hashem , S. 1997. Optimal linear combinations of neural networks. Neural Networks 10(4), 599–614.

    Google Scholar

    Hauschild , M. & Pelikan , M. 2011. An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation 1(3), 111–128.

    Google Scholar

    Hernández , L. C., Hernández , A. M., Cardoso , G. M. C. & Jiménez , Y. M. 2015. Genetic algorithms with diversity measures to build classifier systems. Investigación Operacional 36(3), 206–225.

    Google Scholar

    Ishibuchi , H. & Yamamoto , T. 2003. Evolutionary multiobjective optimization for generating an ensemble of fuzzy rule-based classifiers. In Conference on Genetic and Evolutionary Computation, 197–197. ACM.

    Google Scholar

    Jackowski , K. 2014. Fixed-size ensemble classifier system evolutionarily adapted to a recurring context with an unlimited pool of classifiers. Pattern Analysis and Applications 17(4), 709–724.

    Google Scholar

    Jackowski , K. 2015. Adaptive splitting and selection algorithm for regression. New Generation Computing 33(4), 425–448.

    Google Scholar

    Jackowski , K., Krawczyk , B. & Woźniak , M. 2014. Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. International Journal of Neural Systems 24 (3), 1430007.

    Google Scholar

    Joardar , S., Chatterjee , A., Bandyopadhyay , S. & Maulik , U. 2017. Multi-size patch based collaborative representation for Palm Dorsa Vein Pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm. Engineering Applications of Artificial Intelligence 60, 151–163.

    Google Scholar

    Kaiping , L., Binglian , C., Yan , D. & Ying , H. 2013. A genetic neural network ensemble prediction model based on locally linear embedding for typhoon intensity. In Conference on Industrial Electronics and Applications, 137–142. IEEE.

    Google Scholar

    Karakatič , S., Heričko , M. & Podgorelec , V. 2015. Weighting and sampling data for individual classifiers and bagging with genetic algorithms. In International Joint Conference on Computational Intelligence, 180–187. IEEE.

    Google Scholar

    Kapp , M. N., Sabourin , R. & Maupin , P. 2010. Adaptive incremental learning with an ensemble of support vector machines. In International Conference on Pattern Recognition, 4048–4051. IEEE.

    Google Scholar

    Kapp , M. N., Sabourin , R. & Maupin , P. 2011. A dynamic optimization approach for adaptive incremental learning. International Journal of Intelligent Systems 26(11), 1101–1124.

    Google Scholar

    Khamis , A., Xu , Y., Dong , Z. Y. & Zhang , R. 2016. Faster detection of microgrid islanding events using an adaptive ensemble classifier. IEEE Transactions on Smart Grid 9(3), 1889–1899.

    Google Scholar

    Kim , K.-J. & Cho , S.-B. 2005. DNA gene expression classification with ensemble classifiers optimized by speciated genetic algorithm. In Pattern Recognition and Machine Intelligence, 3776, 649–653.

    Google Scholar

    Kim , K.-J. & Cho , S.-B. 2008a. An evolutionary algorithm approach to optimal ensemble classifiers for DNA microarray data analysis. IEEE Transactions on Evolutionary Computation 12(3), 377–388.

    Google Scholar

    Kim , K.-J. & Cho , S.-B. 2008b. Evolutionary ensemble of diverse artificial neural networks using speciation. Neurocomputing 71(7), 1604–1618.

    Google Scholar

    Kim , K.-J. & Cho , S.-B. 2015. Meta-classifiers for high-dimensional, small sample classification for gene expression analysis. Pattern Analysis and Applications 18(3), 553–569.

    Google Scholar

    Kim , Y., Street , W. N. & Menczer , F. 2002. Meta-evolutionary ensembles. In International Joint Conference on Neural Networks, 2791–2796. IEEE.

    Google Scholar

    Kiranyaz , S., Ince , T., Zabihi , M. & Ince , D. 2014. Automated patient-specific classification of long-term electroencephalography. Journal of Biomedical Informatics 49, 16–31.

    Google Scholar

    Kittler , J., Hatef , M., Duin , R. P. & Matas , J. 1998. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239.

    Google Scholar

    Ko , A. H.-R., Sabourin , R. & Britto Jr., A. d. S. 2006. Evolving ensemble of classifiers in random subspace. In Conference on Genetic and Evolutionary Computation, 1473–1480. ACM.

    Google Scholar

    Kordík , P., Černý , J. & Frýda , T. 2018. Discovering predictive ensembles for transfer learning and meta-learning. Machine Learning 107, 177–207.

    Google Scholar

    Kotsiantis , S. B. 2014. Bagging and boosting variants for handling classifications problems: a survey. The Knowledge Engineering Review 29(1), 78–100.

    Google Scholar

    Krawczyk , B., Galar , M., Jeleń , Ł. & Herrera , F. 2016. Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Applied Soft Computing 38, 714–726.

    Google Scholar

    Krawczyk , B. & Schaefer , G. 2014. Breast thermogram analysis using classifier ensembles and image symmetry features. IEEE Systems Journal 8(3), 921–928.

    Google Scholar

    Krawczyk , B., Schaefer , G. & Woźniak , M. 2013. A cost-sensitive ensemble classifier for breast cancer classification. In International Symposium on Applied Computational Intelligence and Informatics, 427–430. IEEE.

    Google Scholar

    Krawczyk , B., Schaefer , G. & Woźniak , M. 2015. A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification. Artificial Intelligence in Medicine 65(3), 219–227.

    Google Scholar

    Krawczyk , B. & Woźniak , M. 2014. Evolutionary cost-sensitive ensemble for malware detection. In SOCO/CISIS/ICEUTE, 433–442. Springer.

    Google Scholar

    Krawczyk , B., Woźniak , M. & Schaefer , G. 2014. Cost-sensitive decision tree ensembles for effective imbalanced classification. Applied Soft Computing 14, 554–562.

    Google Scholar

    Krawczyk , B., Minku , L. L., Gama , J., Stefanowski , J. & Woźniak , M. 2017. Ensemble learning for data stream analysis: a survey. Information Fusion 37, 132–156.

    Google Scholar

    Krithikaa , M. & Mallipeddi , R. 2016. Differential evolution with an ensemble of low-quality surrogates for expensive optimization problems. In Congress on Evolutionary Computation, 78–85. IEEE.

    Google Scholar

    Krogh , A. & Vedelsby , J. 1995. Neural network ensembles, cross validation, and active learning. In Advances in Neural Information Processing Systems, 7, 231–238.

    Google Scholar

    Kumar , G. & Kumar , K. 2013. Design of an evolutionary approach for intrusion detection. The Scientific World Journal 2013, 1–14.

    Google Scholar

    Kumar , M., Husian , M., Upreti , N. & Gupta , D. 2010. Genetic algorithm: review and application. International Journal of Information Technology 2 (2), 451–454.

    Google Scholar

    Kuncheva , L. I. & Whitaker , C. J. 2003. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51(2), 181–207.

    Google Scholar

    Lacy , S. E., Lones , M. A. & Smith , S. L. 2015a. A comparison of evolved linear and non-linear ensemble vote aggregators. In Congress on Evolutionary Computation, 758–763. IEEE.

    Google Scholar

    Lacy , S. E., Lones , M. A. & Smith , S. L. 2015b. Forming classifier ensembles with multimodal evolutionary algorithms. In Congress on Evolutionary Computation, 723–729. IEEE.

    Google Scholar

    Lévesque , J.-C., Durand , A., Gagné , C. & Sabourin , R. 2012. Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space. In Conference on Genetic and Evolutionary Computation, 879–886. ACM.

    Google Scholar

    Liew , W. S., Loo , C. K. & Obo , T. 2017. Optimizing FELM ensembles using GA-BIC. In Joint World Congress of International Fuzzy Systems Association and International Conference on Soft Computing and Intelligent Systems, 1–6. IEEE.

    Google Scholar

    Lima , T. P. F. & Ludermir , T. B. 2015. Differential evolution and meta-learning for dynamic ensemble of neural network classifiers. In International Joint Conference on Neural Networks, 1–5. IEEE.

    Google Scholar

    Liu , K., Tong , M., Xie , S. & Zeng , Z. 2014a. Fusing decision trees based on genetic programming for classification of microarray datasets. In International Conference on Intelligent Computing, 126–134. Springer.

    Google Scholar

    Liu , K.-H., Huang , D.-S. & Zhang , J. 2007. Microarray data prediction by evolutionary classifier ensemble system. In Congress on Evolutionary Computation, 634–637. IEEE.

    Google Scholar

    Liu , K.-H., Li , B., Zhang , J. & Du , J.-X. 2009. Ensemble component selection for improving ICA based microarray data prediction models. Pattern Recognition 42(7), 1274–1283.

    Google Scholar

    Liu , K.-H., Tong , M., Xie , S.-T. & Yee Ng , V. T. 2015. Genetic programming based ensemble system for microarray data classification. Computational and Mathematical Methods in Medicine 2015, 1–11.

    Google Scholar

    Liu , N., Cao , J., Lin , Z., Pek , P. P., Koh , Z. X. & Ong , M. E. H. 2014b. Evolutionary voting-based extreme learning machines. Mathematical Problems in Engineering 2014, 1–7.

    Google Scholar

    Liu , Y., Chen , W., Hu , J., Zheng , X. & Shi , Y. 2017. Ensemble of surrogates with an evolutionary multi-agent system. In International Conference on Computer Supported Cooperative Work in Design, 521–525. IEEE.

    Google Scholar

    Lones , M. A., Smith , S. L., Alty , J. E., Lacy , S. E., Possin , K. L., Jamieson , D. R. S. & Tyrrell , A. M. 2014. Evolving classifiers to recognize the movement characteristics of Parkinson’s disease patients. IEEE Transactions on Evolutionary Computation 18 (4), 559–576.

    Google Scholar

    Ma , N., Fujita , H., Zhai , Y. & Wang , S. 2015. Ensembles of fuzzy cognitive map classifiers based on quantum computation. Acta Polytechnica Hungarica 12(4), 7–26.

    Google Scholar

    Mabu , S., Obayashi , M. & Kuremoto , T. 2014. Ensemble learning of rule-based evolutionary algorithm using multi layer perceptron for stock trading models. In Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems, 624–629. IEEE.

    Google Scholar

    Mabu , S., Obayashi , M. & Kuremoto , T. 2015. Ensemble learning of rule-based evolutionary algorithm using multi-layer perceptron for supporting decisions in stock trading problems. Applied Soft Computing 36, 357–367.

    Google Scholar

    Mauša , G. & Grbac , T. G. 2017. Co-evolutionary multi-population genetic programming for classification in software defect prediction: an empirical case study. Applied Soft Computing 55, 331–351.

    Google Scholar

    Mehdiyev , N., Krumeich , J., Werth , D. & Loos , P. 2015. Sensor event mining with hybrid ensemble learning and evolutionary feature subset selection model. In International Conference on Big Data, 2159–2168. IEEE.

    Google Scholar

    Mendes-Moreira , J. A., Soares , C., Jorge , A. M. & de Sousa , J. F. 2012. Ensemble approaches for regression: a survey. ACM Computing Surveys 45(1), 10:1–10:40.

    Google Scholar

    Milliken , M., Bi , Y., Galway , L. & Hawe , G. 2016. Multi-objective optimization of base classifiers in stackingC by NSGA-II for intrusion detection. In Symposium Series on Computational Intelligence, 1–8. IEEE.

    Google Scholar

    Neoh , S. C., Zhang , L., Mistry , K., Hossain , M. A., Lim , C. P., Aslam , N. & Kinghorn , P. 2015. Intelligent facial emotion recognition using a layered encoding cascade optimization model. Applied Soft Computing 34, 72–93.

    Google Scholar

    Obo , T., Kubota , N. & Loo , C. K. 2016. Evolutionary ensemble learning of fuzzy randomized neural network for posture recognition. In World Automation Congress, 1–6. IEEE.

    Google Scholar

    Oehmcke , S., Heinermann , J. & Kramer , O. 2015. Analysis of diversity methods for evolutionary multi-objective ensemble classifiers. In European Conference on the Applications of Evolutionary Computation, 567–578. Springer.

    Google Scholar

    Ojha , V. K., Abraham , A. & Snášel , V. 2017. Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming. Applied Soft Computing 52, 909–924.

    Google Scholar

    Ojha , V. K., Jackowski , K., Abraham , A. & Snášel , V. 2014. Feature selection and ensemble of regression models for predicting the protein macromolecule dissolution profile. In World Congress on Nature and Biologically Inspired Computing, 121–126. IEEE.

    Google Scholar

    Ojha , V. K., Jackowski , K., Abraham , A. & Snášel , V. 2015. Dimensionality reduction, and function approximation of poly (Lactic-co-glycolic acid) micro-and nanoparticle dissolution rate. International Journal of Nanomedicine 10, 1119.

    Google Scholar

    Olson , R. S., Bartley , N., Urbanowicz , R. J. & Moore , J. H. 2016. Evaluation of a tree-based pipeline optimization tool for automating data science. In Conference on Genetic and Evolutionary Computation, 485–492. ACM.

    Google Scholar

    Olvera-López , J. A., Carrasco-Ochoa , J. A., Martnez-Trinidad , J. & Kittler , J. 2010. A review of instance selection methods. Artificial Intelligence Review 34(2), 133–143.

    Google Scholar

    Onan , A., Korukoğlu , S. & Bulut , H. 2016. A multiobjective weighted voting ensemble classifier based on differential evolution algorithm for text sentiment classification. Expert Systems with Applications 62, 1–16.

    Google Scholar

    Opitz , D. & Maclin , R. 1999. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 11, 169–198.

    Google Scholar

    Opitz , D. W. 1999. Feature selection for ensembles. In National Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence Conference, 384. American Association for Artificial Intelligence.

    Google Scholar

    Oza , N. C. & Tumer , K. 2008. Classifier ensembles: select real-world applications. Information Fusion 9(1), 4–20.

    Google Scholar

    Pagano , C., Granger , E., Sabourin , R. & Gorodnichy , D. O. 2012. Detector ensembles for face recognition in video surveillance. In International Joint Conference on Neural Networks, 1–8. IEEE.

    Google Scholar

    Parhizkar , E. & Abadi , M. 2015a. BeeOWA: a novel approach based on ABC algorithm and induced OWA operators for constructing one-class classifier ensembles. Neurocomputing 166, 367–381.

    Google Scholar

    Parhizkar , E. & Abadi , M. 2015b. OC-WAD: a one-class classifier ensemble approach for anomaly detection in web traffic. In Iranian Conference on Electrical Engineering, 631–636. IEEE.

    Google Scholar

    Park , C. & Cho , S.-B. 2003a. Evolutionary ensemble classifier for lymphoma and colon cancer classification. In Congress on Evolutionary Computation, 2378–2385. IEEE.

    Google Scholar

    Park , C. & Cho , S.-B. 2003b. Evolutionary computation for optimal ensemble classifier in lymphoma cancer classification. In Foundations of Intelligent Systems, 2871, 521–530.

    Google Scholar

    Pedregosa , F., Varoquaux , G., Gramfort , A., Michel , V., Thirion , B., Grisel , O., Blondel , M., Prettenhofer , P., Weiss , R., Dubourg , V., Vanderplas , J., Passos , A., Cournapeau , D., Brucher , M., Perrot , M. & Duchesnay , E. 2011. Scikit-learn: machine learning in python. Journal of Machine Learning Research 12, 2825–2830.

    Google Scholar

    Peimankar , A., Weddell , S. J., Jalal , T. & Lapthorn , A. C. 2016. Ensemble classifier selection using multi-objective PSO for fault diagnosis of power transformers. In Congress on Evolutionary Computation, 3622–3629. IEEE.

    Google Scholar

    Peimankar , A., Weddell , S. J., Jalal , T. & Lapthorn , A. C. 2017. Evolutionary multi-objective fault diagnosis of power transformers. Swarm and Evolutionary Computation 36, 62–75.

    Google Scholar

    Pourtaheri , Z. K. & Zahiri , S. H. 2016. Ensemble classifiers with improved overfitting. In Conference on Swarm Intelligence and Evolutionary Computation, 93–97. IEEE.

    Google Scholar

    Rahman , A. & Verma , B. 2013a. Cluster based ensemble classifier generation by joint optimization of accuracy and diversity. International Journal of Computational Intelligence and Applications 12(4), 1340003.

    Google Scholar

    Rahman , A. & Verma , B. 2013b. Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm. In International Joint Conference on Neural Networks, 1–6. IEEE.

    Google Scholar

    Rahman , A. & Verma , B. 2013c. Ensemble classifier generation using non-uniform layered clustering and genetic algorithm. Knowledge-Based Systems 43, 30–42.

    Google Scholar

    Rapakoulia , T., Theofilatos , K., Kleftogiannis , D., Likothanasis , S., Tsakalidis , A. & Mavroudi , S. 2014. EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms. Bioinformatics 30(16), 2324–2333.

    Google Scholar

    Redel-Macías , M. D., Fernández-Navarro , F., Gutiérrez , P. A., Cubero-Atienza , A. J. & Hervás-Martínez , C. 2013. Ensembles of evolutionary product unit or RBF neural networks for the identification of sound for pass-by noise test in vehicles. Neurocomputing 109, 56–65.

    Google Scholar

    Roebber , P. J. 2015. Adaptive evolutionary programming. Monthly Weather Review 143(5), 1497–1505.

    Google Scholar

    Rokach , L. 2010. Ensemble-based classifiers. Artificial Intelligence Review 33(1), 1–39.

    Google Scholar

    Rosales-Pérez , A., Gonzalez , J. A., Coello , C. A. C., Escalante , H. J. & Reyes-Garcia , C. A. 2014. Multi-objective model type selection. Neurocomputing 146, 83–94.

    Google Scholar

    Rosales-Pérez , A., García , S., Gonzalez , J. A., Coello , C. A. C. & Herrera , F. 2017. An evolutionary multi-objective model and instance selection for support vector machines with pareto-based ensembles. IEEE Transactions on Evolutionary Computation 21(6), 863–877.

    Google Scholar

    Sagi , O. & Rokach , L. 2018. Ensemble learning: a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(4), e1249.

    Google Scholar

    Saha , S., Mitra , S. & Yadav , R. K. 2016. A multiobjective based automatic framework for classifying cancer-microRNA biomarkers. Gene Reports 4, 91–103.

    Google Scholar

    Saleh , R., Farsi , H. & Zahiri , S. H. 2016. Ensemble classification of PolSAR data using multi-objective heuristic combination rule. In Conference on Swarm Intelligence and Evolutionary Computation, 88–92. IEEE.

    Google Scholar

    Santu , S. K. K., Rahman , M. M., Islam , M. M. & Murase , K. 2014. Towards better generalization in Pittsburgh learning classifier systems. In Congress on Evolutionary Computation, 1666–1673. IEEE.

    Google Scholar

    Schaefer , G. 2013. Evolutionary optimisation of classifiers and classifier ensembles for cost-sensitive pattern recognition. In International Symposium on Applied Computational Intelligence and Informatics, 343–346. IEEE.

    Google Scholar

    Schapire , R. E. 1999. A brief introduction to boosting. In International Joint Conference on Artificial Intelligence, 1401–1406. European Association for Artificial Intelligence.

    Google Scholar

    Schuman , C. D., Birdwell , J. D. & Dean , M. E. 2014. Spatiotemporal classification using neuroscience-inspired dynamic architectures. Procedia Computer Science 41, 89–97.

    Google Scholar

    Shunmugapriya , P. & Kanmani , S. 2013. Optimization of stacking ensemble configurations through artificial bee colony algorithm. Swarm and Evolutionary Computation 12, 24–32.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2012. Differential evolution based feature selection and classifier ensemble for named entity recognition. In International Conference on Computational Linguistics, 2475–2490. International Committee on Computational Linguistics.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2013. Differential evolution based mention detection for anaphora resolution. In India Conference, 1–6. IEEE.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2014a. Differential evolution based multiobjective optimization for biomedical entity extraction. In International Conference on Advances in Computing, Communications and Informatics, 1039–1044. IEEE.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2014b. Entity extraction in biochemical text using multiobjective optimization. Computación y Sistemas 18(3), 591–602.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2015. MODE: multiobjective differential evolution for feature selection and classifier ensemble. Soft Computing 19(12), 3529–3549.

    Google Scholar

    Sikdar , U. K., Ekbal , A. & Saha , S. 2016. A generalized framework for anaphora resolution in Indian languages. Knowledge-Based Systems 109, 147–159.

    Google Scholar

    Singh , I., Sanwal , K. & Praveen , S. 2016. Breast cancer detection using two-fold genetic evolution of neural network ensembles. In International Conference on Data Science and Engineering, 1–6. IEEE.

    Google Scholar

    Stefano , C. D., Fontanella , F., Folino , G. & Freca , A. 2011. A Bayesian approach for combining ensembles of GP classifiers. In International Workshop on Multiple Classifier Systems, 26–35. Springer.

    Google Scholar

    Tabassum , N. & Ahmed , T. 2016. A theoretical study on classifier ensemble methods and its applications. In International Conference on Computing for Sustainable Global Development, 374–378. IEEE.

    Google Scholar

    Tan , C. J., Lim , C. P. & Cheah , Y.-N. 2014. A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models. Neurocomputing 125, 217–228.

    Google Scholar

    Tang , H. L., Goh , J., Peto , T., Ling , B. W.-K., Al turk , L. I., Hu , Y., Wang , S. & Saleh , G. M. 2013. The reading of components of diabetic retinopathy: an evolutionary approach for filtering normal digital fundus imaging in screening and population based studies. PloS One 8(7), e66730.

    Google Scholar

    Tian , J. & Feng , N. 2014. Adaptive generalized ensemble construction with feature selection and its application in recommendation. International Journal of Computational Intelligence Systems 7(sup2), 35–43.

    Google Scholar

    Trawiński , K., Cordón , O., Quirin , A. & Sánchez , L. 2013. Multiobjective genetic classifier selection for random oracles fuzzy rule-based classifier ensembles: how beneficial is the additional diversity? Knowledge-Based Systems 54, 3–21.

    Google Scholar

    Trawiński , K., Cordón , O. & Quirin , A. 2014. Embedding evolutionary multiobjective optimization into fuzzy linguistic combination method for fuzzy rule-based classifier ensembles. In International Conference on Fuzzy Systems, 1968–1975. IEEE.

    Google Scholar

    Trivedi , S. K. & Dey , S. 2014. A study of ensemble based evolutionary classifiers for detecting unsolicited emails. In Conference on Research in Adaptive and Convergent Systems, 46–51. ACM.

    Google Scholar

    Tsakonas , A. 2014. An analysis of accuracy-diversity trade-off for hybrid combined system with multiobjective predictor selection. Applied Intelligence 40(4), 710–723.

    Google Scholar

    Tsakonas , A. & Gabrys , B. 2013. A fuzzy evolutionary framework for combining ensembles. Applied Soft Computing 13(4), 1800–1812.

    Google Scholar

    Vaiciukynas , E., Verikas , A., Gelzinis , A., Bacauskiene , M., Kons , Z., Satt , A. & Hoory , R. 2014. Fusion of voice signal information for detection of mild laryngeal pathology. Applied Soft Computing 18, 91–103.

    Google Scholar

    Veeramachaneni , K., Derby , O., Sherry , D. & O’Reilly , U.-M. 2013. Learning regression ensembles with genetic programming at scale. In Conference on Genetic and Evolutionary Computation, 1117–1124. ACM.

    Google Scholar

    Vega-Pons , S. & Ruiz-Shulcloper , J. 2011. A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence 25(3), 337–372.

    Google Scholar

    Vluymans , S., Triguero , I., Cornelis , C. & Saeys , Y. 2016. EPRENNID: an evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. Neurocomputing 216, 596–610.

    Google Scholar

    Vukobratović , B. & Struharik , R. 2017. Hardware acceleration of nonincremental algorithms for the induction of decision trees. In Telecommunication Forum, 1–8. IEEE.

    Google Scholar

    Wang , D. & Alhamdoosh , M. 2013. Evolutionary extreme learning machine ensembles with size control. Neurocomputing 102, 98–110.

    Google Scholar

    Wen , Y.-W. & Ting , C.-K. 2016. Learning ensemble of decision trees through multifactorial genetic programming. In Congress on Evolutionary Computation, 5293–5300. IEEE.

    Google Scholar

    Winkler , S., Schaller , S., Dorfer , V., Affenzeller , M., Petz , G. & Karpowicz , M. 2015. Data-based prediction of sentiments using heterogeneous model ensembles. Soft Computing 19(12), 3401–3412.

    Google Scholar

    Wistuba , M., Schilling , N. & Schmidt-Thieme , L. 2017. Automatic frankensteining: creating complex ensembles autonomously. In International Conference on Data Mining, 741–749. SIAM.

    Google Scholar

    Witten , I. H., Frank , E., Hall , M. A. & Pal , C. J. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

    Google Scholar

    Wolpert , D. H. 1992. Stacked generalization. Neural Networks 5(2), 241–259.

    Google Scholar

    Wolpert , D. H. & Macready , W. G. 1997. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1), 67–82.

    Google Scholar

    Woon , W. L. & Kramer , O. 2016. Enhanced SVR ensembles for wind power prediction. In International Joint Conference on Neural Networks, 2743–2748. IEEE.

    Google Scholar

    Wozniak , M. 2009. Evolutionary approach to produce classifier ensemble based on weighted voting. In World Congress on Nature and Biologically Inspired Computing, 648–653. IEEE.

    Google Scholar

    Xavier-Júnior , J. A. C., Freitas , A. A., Feitosa-Neto , A. & Ludermir , T. B. 2018. A novel evolutionary algorithm for automated machine learning focusing on classifier ensembles. In Brazilian Conference on Intelligent Systems, São Paulo, Brazil. IEEE.

    Google Scholar

    Xu , H., Caramanis , C. & Mannor , S. 2012. Sparse algorithms are not stable: a no-free-lunch theorem. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(1), 187–193.

    Google Scholar

    Yager , R. R. 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics 18(1), 183–190.

    Google Scholar

    Yao , X. & Islam , M. M. 2008. Evolving artificial neural network ensembles. IEEE Computational Intelligence Magazine 3(1), 31–42.

    Google Scholar

    Zagorecki , A. 2014. Feature selection for Naive Bayesian network ensemble using evolutionary algorithms. In Federated Conference on Computer Science and Information Systems, 381–385. IEEE.

    Google Scholar

    Zhang , L., Mistry , K., Neoh , S. C. & Lim , C. P. 2016a. Intelligent facial emotion recognition using moth-firefly optimization. Knowledge-Based Systems 111, 248–267.

    Google Scholar

    Zhang , W., Qu , Z., Zhang , K., Mao , W., Ma , Y. & Fan , X. 2017a. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energy Conversion and Management 136, 439–451.

    Google Scholar

    Zhang , Y., Liu , B. & Yang , F. 2016b. Differential evolution based selective ensemble of extreme learning machine. In Trustcom/BigDataSE/ISPA, 1327–1333. IEEE.

    Google Scholar

    Zhang , Y., Liu , B., Cai , J. & Zhang , S. 2017b. Ensemble weighted extreme learning machine for imbalanced data classification based on differential evolution. Neural Computing and Applications 28(1), 259–267.

    Google Scholar

    Zhang , Y., Zhang , H., Cai , J. & Yang , B. 2014. A weighted voting classifier based on differential evolution. Abstract and Applied Analysis 2014(1), 1–6.

    Google Scholar

  • Cite this article

    Henry E. L. Cagnini, Silvia C. N. Das Dôres, Alex A. Freitas, Rodrigo C. Barros. 2023. A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000024
    Henry E. L. Cagnini, Silvia C. N. Das Dôres, Alex A. Freitas, Rodrigo C. Barros. 2023. A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000024
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A survey of evolutionary algorithms for supervised ensemble learning

  • Received: 27 October 2021
  • Revised: 11 January 2023
  • Accepted: 11 January 2023
  • Published online: 01 March 2023
The Knowledge Engineering Review  38,  2023, 38(1)  |  Cite this article

Abstract: Abstract: This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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    Acknowledgments

    • This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. The authors would also like to acknowledge FAPERGS for partially funding this research.

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    • Available at http://dl.acm.org.

    • In supervised learning it is common to divide a dataset into three disjoint sets: training, validation and test. The validation set is used to evaluate the quality of models while training and helps to prevent overfitting to the training data. The test set is used for the final model evaluation after training.

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    Henry E. L. Cagnini, Silvia C. N. Das Dôres, Alex A. Freitas, Rodrigo C. Barros. 2023. A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000024
    Henry E. L. Cagnini, Silvia C. N. Das Dôres, Alex A. Freitas, Rodrigo C. Barros. 2023. A survey of evolutionary algorithms for supervised ensemble learning. The Knowledge Engineering Review 38(1), doi: 10.1017/S0269888923000024
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