|
Aksela M., Laaksonen J.2006. Using diversity of errors for selecting members of a committee classifier. Journal of Pattern Recognition39(4), 608–623.
Google Scholar
|
|
Alaiz-Rodriguez R.2008. Local decision bagging of binary neural classifiers. Lecture Notes in Artificial Intelligence5032, 1–12.
Google Scholar
|
|
Amores J., Sebe N., Radeva P.2006. Boosting the distance estimation application to the K-nearest neighbor classifier. Pattern Recognition Letters27, 201–209.
Google Scholar
|
|
Babenko B., Yang M. H., Belongie S.2009. A family of online boosting algorithms. In IEEE 12th International Conference on Computer Vision Workshops, September 27–October 4, Kyoto, 1346–1353.
Google Scholar
|
|
Bakker B., Heskes T.2003. Clustering ensembles of neural network models. Neural Networks16(2), 261–269.
Google Scholar
|
|
Banfield R. E., Hall L. O., Bowyer K. W.2007. A comparison of decision tree ensemble creation techniques. IEEE Transactions Pattern Analysis and Machine Intelligence29, 173–180.
Google Scholar
|
|
Banfield R. E., Hall L. O., Bowyer K. W., Kegelmeyer W. P.2005. Ensemble diversity measures and their application to thinning. Information Fusion6(1), 49–62.
Google Scholar
|
|
Baszczyski J., Sowiski R., Stefanowski J.2010. Variable consistency bagging ensembles. Lecture Notes in Computer Science5946, 40–52.
Google Scholar
|
|
Bauer E., Kohavi R.1999. An empirical comparison of voting classification algorithms: bagging, voting, and variants. Machine Learning36, 105–139.
Google Scholar
|
|
Bifet A., Holmes G., Pfahringer B.2010a. Leveraging bagging for evolving data streams. Lecture Notes in Artificial Intelligence6321, 135–150.
Google Scholar
|
|
Bifet A., Holmes G., Kirkby R., Pfahringer B.2010b. MOA: massive online analysis. Journal of Machine Learning Research11, 1601–1604.
Google Scholar
|
|
Bifet A., Holmes G., Pfahringer B., Kirkby R., Gavalda R.2009a. New ensemble methods for evolving data streams. In 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, 139–148.
Google Scholar
|
|
Bifet A., Holmes G., Pfahringer B., Gavalda R.2009b. Improving adaptive bagging methods for evolving data streams. Lecture Notes in Artificial Intelligence5828, 23–37.
Google Scholar
|
|
Bradley J. K., Schapire R. E.2008. Filterboost: regression and classication on large datasets. Advances in Neural Information Processing Systems20, 185–192.
Google Scholar
|
|
Breiman L.1996. Bagging predictors. Machine Learning24, 123–140.
Google Scholar
|
|
Breiman L.1999. Pasting small votes for classification in large databases and on-line. Machine Learning36(1–2), 85–103.
Google Scholar
|
|
Breiman L.1999b.Prediction games and arcing algorithms. Neural Computation11(7), 1493–1517.
Google Scholar
|
|
Breiman L.2000. Randomizing outputs to increase prediction accuracy. Machine Learning40, 229–242.
Google Scholar
|
|
Breiman L.2001. Random forests. Machine Learning45(1), 5–32.
Google Scholar
|
|
Brown G., Wyatt J., Harris R., Yao X.2005. Diversity creation methods: a survey and categorisation. Information Fusion6(1), 5–20.
Google Scholar
|
|
Bryll R., Gutierrez-Osuna R., Quek F.2003. Attribute bagging: improving accuracy of classifier ensemble by using random feature subsets. Pattern Recognition36, 1291–1302.
Google Scholar
|
|
Brzezinski D., Stefanowski J.2013. Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Transactions on Neural Networks and Learning Systems24(7), in press.
Google Scholar
|
|
Buhlmann P.2012. Bagging, boosting and ensemble methods. Handbook of Computational Statistics – Springer Handbooks of Computational Statistics, 985–1022.
Google Scholar
|
|
Buhlmann P., Yu B.2002. Analyzing bagging. The Annals of Statistics30(4), 927–961.
Google Scholar
|
|
Buja W. S.2006. Observations on bagging. Statistica Sinica16, 323–351.
Google Scholar
|
|
Cai Q-T., Chun-Yi P., Chang-Shui Z.2008a. A weighted subspace approach for improving bagging performance. In IEEE International Conference on Acoustics, Speech and Signal Processing, March 31–April 4, Las Vegas, 3341–3344.
Google Scholar
|
|
Cai Q-T., Chun-Yi P., Chang-Shui Z.2008b. Cost-sensitive boosting algorithms as gradient descent. In IEEE International Conference on Acoustics, Speech and Signal Processing, March 31–April 4, Las Vegas, 2009–2012.
Google Scholar
|
|
Chawla N. V., Hall L. O., Bowyer K. W., Kegelmeyer W. P.2002. SMOTE: synthetic minority oversampling technique. Journal of Artificial Intelligence Research16, 321–357.
Google Scholar
|
|
Chawla N. V., Lazarevic A., Hall L. O., Bowyer K.2003. SMOTE-Boost: improving prediction of the minority class in boosting. Principles of Knowledge Discovery in Databases, PKDD-2003, 107–119.
Google Scholar
|
|
Coelho A., Nascimento D.2010. On the evolutionary design of heterogeneous bagging models. Neurocomputing73(16–18), 3319–3322.
Google Scholar
|
|
Croux C., Joossens K., Lemmens A.2007. Trimmed bagging. Computational Statistics and Data Analysis52, 362–368.
Google Scholar
|
|
Derbeko P., Yaniv R., Meir R.2002. Variance optimized bagging. Lecture Notes in Artificial Intelligence2430, 60–72.
Google Scholar
|
|
Dietterich T.2000. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning40, 139–157.
Google Scholar
|
|
Domingos P.2000. A unified bias-variance decomposition for zero-one and squared loss. In Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, 564–569.
Google Scholar
|
|
Elwel R., Polikar R.2011. Incremental learning of concept drift in nonstationary environments. IEEE Transactions on Neural Networks22(10), 1517–1531.
Google Scholar
|
|
Esposito R., Saitta L.2003. Monte Carlo theory as an explanation of bagging and boosting. In 18th International Joint Conference on Artificial Intelligence IJCAI'03, Acapulco, Mexico, 499–504.
Google Scholar
|
|
Faisal Z., Uddin M. M., Hirose H.2010. On selecting additional predictive models in double bagging type ensemble method. Lecture Notes in Computer Science6019, 199–208.
Google Scholar
|
|
Fan W., Stolfo S. J., Zhang J., Chan P. K.1999. AdaCost: misclassification cost-sensitive boosting. In 16th International Conference on Machine Learning, Slovenia, 97–105.
Google Scholar
|
|
Frank E., Pfahringer B.2006. Improving on bagging with input smearing. Lecture Notes in Artificial Intelligence3918, 97–106.
Google Scholar
|
|
Freund Y., Schapire R. E.1996a. Experiments with a new boosting algorithm. In 13th International Conference on Machine Learning, Bari, Italy, 148–156.
Google Scholar
|
|
Freund Y., Schapire R. E.1996b. Game theory, on-line prediction and boosting. In Ninth Annual Conference On Computational Learning Theory-COLT '96, Desenzano sul Garda, Italy, 325–332.
Google Scholar
|
|
Freund Y., Schapire R. E.1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences55(1), 119–139.
Google Scholar
|
|
Friedman J. H., Hall P.2007. On bagging and nonlinear estimation. Journal of Statistical Planning and Inference137(3), 669–683.
Google Scholar
|
|
Friedman J. H., Hastie T., Tibshirani R.2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics38, 367–378.
Google Scholar
|
|
Fu Q., Hu S. X., Zhao S. Y.2005. Clustering-based selective neural network ensemble. Journal of Zhejiang University Science6A(5), 387–392.
Google Scholar
|
|
Fumera G., Roli F., Serrau A.2005. Dynamics of variance reduction in bagging and other techniques based on randomisation. Lecture Notes in Computer Science3541, 316–325.
Google Scholar
|
|
Fumera G., Roli F., Serrau A.2008. A theoretical analysis of bagging as a linear combination of classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence30(7), 1293–1299.
Google Scholar
|
|
Fushiki T.2010. Bayesian bootstrap prediction. Journal of Statistical Planning and Inference140, 65–74.
Google Scholar
|
|
Galar M., Fernandez A., Barrenechea E., Bustince H., Herrera F.2012. A review on ensembles for the class imbalance problem: bagging-boosting and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews42(4), 463–484.
Google Scholar
|
|
Gama J.2010. Knowledge Discovery from Data Streams. CRC Press.
Google Scholar
|
|
Gambina A., Szczureka E., Dutkowski J., Bakunc M., Dadlezc M.2009. Classification of peptidemass fingerprint data by novel no-regret boosting method. Computers in Biology and Medicine39, 460–473.
Google Scholar
|
|
Gao W., Zhou Z-H.2010. Approximation Stability and Boosting. Lecture Notes in Artificial Intelligence6331, 59–73.
Google Scholar
|
|
Gao Y., Gao F., Guan X.2010. Improved boosting algorithm with adaptive filtration. In 8th World Congress on Intelligent Control and Automation, July 6–9, Jinan, China, 3173–3178.
Google Scholar
|
|
Garcia-Pedrajas N.2009. Supervised projection approach for boosting classifiers. Pattern Recognition42, 1742–1760.
Google Scholar
|
|
Garcia-Pedrajas N., Ortiz-Boyer D.2008. Boosting random subspace method. Neural Networks21, 1344–1362.
Google Scholar
|
|
Garcia-Pedrajas N., Ortiz-Boyer D.2009. Boosting k-nearest neighbor classifier by means of input space projection. Expert Systems with Applications36, 10570–10582.
Google Scholar
|
|
Gomez-Verdejo V., Ortega-Moral M., Arenas-Garcia J., Figueiras-Vidal A.2006. Boosting by weighting critical and erroneous samples. Neurocomputing69, 679–685.
Google Scholar
|
|
Grabner H., Bischof H.2006. On-line boosting and vision. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 17–22, 260–267.
Google Scholar
|
|
Grandvalet Y.2004. Bagging equalizes inuence. Machine Learning55, 251–270.
Google Scholar
|
|
Hall L., Baneld R., Bowyer K., Kegelmeyer W.2007. Boosting lite—handling larger datasets and slower base classifiers. Lecture Notes in Computer Science4472, 161–170.
Google Scholar
|
|
Hall P., Samworth R. J.2005. Properties of bagged nearest neighbour classifiers. Journal of the Royal Statistical Society Series B67(3), 363–379.
Google Scholar
|
|
Hernandez-Lobato D., Martinez-Munoz G., Suarez A.2007. Out of bootstrap estimation of generalization error curves in bagging ensembles. Lecture Notes in Computer Science4881, 47–56.
Google Scholar
|
|
Hido S., Kashima H., Takahashi Y.2009. Roughly balanced bagging for imbalanced data. Statistical Analysis and Data Mining2, 412–426.
Google Scholar
|
|
Ho T. K.1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence20(8), 832–844.
Google Scholar
|
|
Hothorn T., Lausen B.2003. Double-bagging: combining classifiers by bootstrap aggregation. Pattern Recognition36(6), 1303–1309.
Google Scholar
|
|
Hothorn T., Lausen B.2005. Bundling classifiers by bagging trees. Computational Statistics and Data Analysis49, 1068–1078.
Google Scholar
|
|
Jiang Y., Jin-Jiang L., Gang L., Honghua D., Zhi-Hua Z.2005. Dependency bagging. Lecture Notes in Artificial Intelligence3641, 491–500.
Google Scholar
|
|
Jimnez-Gamero M. D., Muoz-Garca J., Pino-Mejas R.2004. Reduced bootstrap for the median. Statistica Sinica14, 1179–1198.
Google Scholar
|
|
Joshi M. V., Kumar V., Agarwal R. C.2001. Evaluating boosting algorithms to classify rare classes: comparison and improvements. IEEE International Conference on Data Mining, 257–264.
Google Scholar
|
|
Kalai A., Servedio R.2005. Boosting in the presence of noise. Journal of Computer and System Sciences71, 266–290.
Google Scholar
|
|
Khoshgoftaar T. M., Van Hulse J., Napolitano A.2011. Comparing boosting and bagging techniques with noisy and imbalanced data. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans41(3), 552–568.
Google Scholar
|
|
Koco S., Capponi C.2011. A boosting approach to multiview classification with cooperation. In European Conference on Machine Learning and Knowledge Discovery in Databases ECML-PKDD'11, Athens, 209–228.
Google Scholar
|
|
Kotsiantis S., Pintelas P.2004. Combining bagging and boosting. International Journal of Computational Intelligence1(4), 324–333.
Google Scholar
|
|
Kotsiantis S. B., Kanellopoulos D.2010. Bagging different instead of similar models for regression and classification problems. International Journal of Computer Applications in Technology37(1), 20–28.
Google Scholar
|
|
Kotsiantis S. B., Kanellopoulos D., Pintelas P. E.2006. Local boosting of decision stumps for regression and classification problems. Journal of Computers1(4), 30–37.
Google Scholar
|
|
Kotsiantis S. B., Tsekouras G. E., Pintelas P. E.2005. Local bagging of decision stumps. In 18th International Conference on Innovations in Applied Artificial Intelligence, Bari, Italy, 406–411.
Google Scholar
|
|
Kuncheva L. I., Skurichina M., Duin R. P. W.2002. An experimental study on diversity for bagging and boosting with linear classifiers. Information Fusion3, 245–258.
Google Scholar
|
|
Kuncheva L., Whitaker C. J.2002. Using diversity with three variants of boosting: aggressive. Lecture Notes in Computer Science2364, 81–90.
Google Scholar
|
|
Kuncheva L. I., Whitaker C. J.2003. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Journal of Machine Learning51, 181–207.
Google Scholar
|
|
Kuncheva L.2004a. Combining Pattern Classifiers: Methods and Algorithms. Wiley.
Google Scholar
|
|
Kuncheva L.2004b. Classifier ensembles for changing environments. Lecture Notes in Computer Science3077, 1–15.
Google Scholar
|
|
Latinne P., Debeir O., Decaestecker Ch.2000. Mixing bagging and multiple feature subsets to improve classification accuracy of decision tree combination. In 10th Belgian-Dutch Conference on Machine Learning. Tilburg University, 15–22.
Google Scholar
|
|
Le D.-D., Satoh S.2007. Ent-boost: boosting using entropy measures for robust object detection. Pattern Recognition Letters28, 1083–1090.
Google Scholar
|
|
Lee H., Clyde M. A.2004. Lossless online bayesian bagging. Journal of Machine Learning Research5, 143–151.
Google Scholar
|
|
Leistner C., Saffari A., Roth P., Bischof H.2009. On robustness of on-line boosting—a competitive study. In IEEE 12th International Conference on Computer Vision Workshops, Kyoto, Japan, 1362–1369.
Google Scholar
|
|
Leskes B., Torenvliet L.2008. The value of agreement a new boosting algorithm. Journal of Computer and System Sciences74, 557–586.
Google Scholar
|
|
Li C.2007. Classifying imbalanced data using a bagging ensemble variation (BEV). 45th Annual Southeast Regional Conference, 203–208.
Google Scholar
|
|
Li G.-Z., Yang J. Y.2008. Feature selection for ensemble learning and its application. In Machine Learning in Bioinformatics, Zhang, Y.-Q. & Rajapakse, J. C. (eds.). Wiley.
Google Scholar
|
|
Li W., Gao X., Zhu Y., Ramesh V., Boult T.2005. On the small sample performance of boosted classifiers. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20–25, 574–581.
Google Scholar
|
|
Littlestone N., Warmuth M. K.1994. The weighted majority algorithm. Information and Computation108(2), 212–261.
Google Scholar
|
|
Liu X., Yu T.2007. Gradient feature selection for online boosting. In IEEE 11th International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil, 1–8.
Google Scholar
|
|
Lu Y., Tian Q., Huang T.2007. Interactive boosting for image classification. Lecture Notes in Computer Science4472, 180–189.
Google Scholar
|
|
Markowitz H.1959. Portfolio Selection: Efficient Diversification of Investments. Yale University Press.
Google Scholar
|
|
Martinez-Munoz G., Suarez A.2007. Using boosting to prune bagging ensembles. Pattern Recognition Letters28(1), 156–165.
Google Scholar
|
|
Martinez-Munoz G., Suarez A.2010. Out-of-bag estimation of the optimal sample size in bagging. Pattern Recognition43, 143–152.
Google Scholar
|
|
Martinez-Munoz G., Hernandez-Lobato D., Suarez A.2007. Selection of decision stumps in bagging ensembles. Lecture Notes in Computer Science4668, 319–328.
Google Scholar
|
|
Mason L., Baxter J., Bartlett P., Frean M.1999. Functional gradient techniques for combining hypotheses. Advances in Large Margin Classifiers. MIT Press, 221–247.
Google Scholar
|
|
Melville P., Mooney R.2005. Creating diversity in ensembles using artificial data. Information Fusion6, 99–111.
Google Scholar
|
|
Minku L. L., Xin Y.2012. DDD: a new ensemble approach for dealing with concept drift. IEEE Transactions on Knowledge and Data Engineering24(4), 619–633.
Google Scholar
|
|
Nanculef R., Valle C., Allende H., Moraga C.2007. Bagging with asymmetric costs for misclassified and correctly classified examples. Lecture Notes in Computer Science4756, 694–703.
Google Scholar
|
|
Oza N. C.2003. Boosting with averaged weight vectors. Lecture Notes in Computer Science2709, 15–24.
Google Scholar
|
|
Oza N.2005. Online bagging and boosting. In 2005 IEEE International Conference on Systems, Man and Cybernetics, October 10–12, Hawaii, USA, 2340–2345.
Google Scholar
|
|
O'Sullivan J., Langford J., Caruna R., Blum A.2000. Featureboost: a metalearning algorithm that improves model robustness. 17th International Conference on Machine Learning, 703–710.
Google Scholar
|
|
Panov P., Dzeroski S.2007. Combining bagging and random subspaces to create better ensembles. Lecture Notes in Computer Science4723, 118–129.
Google Scholar
|
|
Pelossof R., Jones M., Vovsha I., Rudin C.2009. Online coordinate boosting. In IEEE 12th International Conference on Computer Vision Workshops, Kyoto, Japan, 1354–1361.
Google Scholar
|
|
Peng J., Barbu C., Seetharaman G., Fan W., Wu X., Palaniappan K.2011. ShareBoost: boosting for multi-view learning with performance guarantees. Lecture Notes in Computer Science6912, 597–612.
Google Scholar
|
|
Phama T., Smeulders A.2008. Quadratic boosting. Pattern Recognition41, 331–341.
Google Scholar
|
|
Pino-Mejias R., Jimenez-Gamero M., Cubiles-de-la-Vega M., Pascual-Acosta A.2008. Reduced bootstrap aggregating of learning algorithms. Pattern Recognition Letters29, 265–271.
Google Scholar
|
|
Pino-Mejias R., Cubiles-de-la-Vega M., Lpez-Coello M., Silva-Ramrez E., Jimnez-Gamero M.2004. Bagging classification models with reduced bootstrap. Lecture Notes in Computer Science3138, 966–973.
Google Scholar
|
|
Puuronen S., Skrypnyk I., Tsymbal A.2001. Ensemble feature selection based on contextual merit and correlation heuristics. Lecture Notes in Computer Science2151, 155–168.
Google Scholar
|
|
Redpath D. B., Lebart K.2005. Boosting feature selection. Lecture Notes in Computer Science3686, 305–314.
Google Scholar
|
|
Reyzin L., Schapire R. E.2006. How boosting the margin can also boost classifier complexity. In 23rd International Conference on Machine Learning, Pittsburgh, 753–760.
Google Scholar
|
|
Rodriguez J. J., Kuncheva L. I., Alonso C. J.2006. Rotation forest: a new classier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence28(10), 1619–1630.
Google Scholar
|
|
Rodriguez J. J., Maudes J.2008. Boosting recombined weak classifiers. Pattern Recognition Letters29, 1049–1059.
Google Scholar
|
|
Schapire R., Freund Y., Bartlett P., Lee W. S.1997. Boosting the margin: a new explanation for the effectiveness of voting methods. In Fourteenth International Conference on Machine Learning-ICML'97, 322–330.
Google Scholar
|
|
Schapire R. E., Singer Y.1999. Improved boosting algorithms using confidence-rated predictions. Machine Learning37, 297–336.
Google Scholar
|
|
Seiffert C., Khoshgoftaar T., Hulse J., Napolitano A.2008. Resampling or reweighting: a comparison of boosting implementations. In 20th IEEE International Conference on Tools with Artificial Intelligence-ICTAI'08, Ohio, USA, 445–451.
Google Scholar
|
|
Seni G., Elder J.2010. Ensemble methods in data mining: improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery2(1), 1–126.
Google Scholar
|
|
Servedio R. A.2003. Smooth boosting and learning with malicious noise. The Journal of Machine Learning Research4, 633–648.
Google Scholar
|
|
Shen C., Li H.2010. Boosting through optimization of margin distributions. IEEE Transactions on Neural Networks21(4), 659–667.
Google Scholar
|
|
Shirai S., Kudo M., Nakamura A.2008. Bagging, random subspace method and biding. Lecture Notes in Computer Science5342, 801–810.
Google Scholar
|
|
Shirai S., Kudo M., Nakamura A.2009. Comparison of bagging and boosting algorithms on sample and feature weighting. Lecture Notes in Computer Science5519, 22–31.
Google Scholar
|
|
Skurichina M., Duin R.2000. The role of combining rules in bagging and boosting. Lecture Notes in Computer Science1876, 631–640.
Google Scholar
|
|
Sohn S. Y., Shin H. W.2007. Experimental study for the comparison of classifier combination methods. Pattern Recognition40, 33–40.
Google Scholar
|
|
Stefanowski J.2007. Combining answers of sub-classifiers in the bagging-feature ensembles. Lecture Notes in Artificial Intelligence4585, 574–583.
Google Scholar
|
|
Su X., Khoshgoftarr T. M., Zhu X.2008. VoB predictors: voting on bagging classifications. In 19th International Conference on Pattern Recognition-ICPR'2008, December 8–11, Florida, USA, 1–4.
Google Scholar
|
|
Sun Y., Kamel M. S., Wong A., Wang Y.2007. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition40, 3358–3378.
Google Scholar
|
|
Tang W.2003. Selective ensemble of decision trees. Lecture Notes in Artificial Intelligence2639, 476–483.
Google Scholar
|
|
Tao D., Tang X., Li X., Wu X.2006. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence28(7), 1088–1099.
Google Scholar
|
|
Terabe M., Washio T., Motoda H.2001. The effect of subsampling rate on subagging performance. ECML2001/PKDD2001, 48–55.
Google Scholar
|
|
Ting K., Witten I.1997. Stacking bagged and dagged models. In Fourteenth International Conference on Machine Learning-ICML ’97, Tennessee, USA, 367–375.
Google Scholar
|
|
Torres-Sospedra J., Hernandez-Espinosa C., Fernandez-Redondo M.2007a. Mixing aveboost and conserboost to improve boosting methods. In International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12–17, 672–677.
Google Scholar
|
|
Torres-Sospedra J., Hernandez-Espinosa C., Fernandez-Redondo M.2007b. Designing a multilayer feedforward ensemble with the weighted conservative boosting algorithm. In International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12–17, 684–689.
Google Scholar
|
|
Torres-Sospedra J., Hernandez-Espinosa C., Fernandez-Redondo M.2008. Researching on combining boosting ensembles. In International Joint Conference on Neural Networks-IJCNN2008, Hong Kong, 2290–2295.
Google Scholar
|
|
Tsaoa C., Chang Y. I.2007. A stochastic approximation view of boosting. Computational Statistics and Data Analysis52, 325–334.
Google Scholar
|
|
Tsymbal A., Puuronen S.2000. Bagging and boosting with dynamic integration of classifiers. Lecture Notes in Artificial Intelligence1910, 116–125.
Google Scholar
|
|
Valentini G., Masuli F.2002. Ensembles of learning machines. Lecture Notes in Computer Science2486, 3–19.
Google Scholar
|
|
Valentini G., Dietterich T. G.2003. Low bias bagged support vector machines. In 20th International Conference on Machine Learning ICML-2003, Washington, USA, 752–759.
Google Scholar
|
|
Vezhnevets A., Barinova O.2007. Avoiding boosting overfitting by removing confusing shamples. In ECML2007, Poland, September, 430–441.
Google Scholar
|
|
Wall R., Cunningham P., Walsh P., Byrne S.2003. Explaining the output of ensembles in medical decision support on a case by case basis. Artificial Intelligence in Medicine28(2), 191–206.
Google Scholar
|
|
Wang X., Wang H.2006. Classification by evolutionary ensembles. Pattern Recognition39, 595–607.
Google Scholar
|
|
Wang C.-M., Yang H.-Z., Li F.-C., Fu R.-X.2006. Two stages based adaptive sampling boosting method. In Fifth International Conference on Machine Learning and Cybernetics, Dalian, August 13–16, 2925–2927.
Google Scholar
|
|
Wang S., Yao X.2009. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium on Computational Intelligence and Data Mining, 324–331.
Google Scholar
|
|
Wang W., Zhou Z.-H.2010. A new analysis of co-training. In 27th International Conference on Machine Learning-ICML'10, Haifa, Israel, 1135–1142.
Google Scholar
|
|
Webb G. I.2000. MultiBoosting: a technique for combining boosting and wagging. Machine Learning40, 159–196.
Google Scholar
|
|
Wolpert D. H.1994. Stacked generalization. Neural Networks5(6), 1289–1301.
Google Scholar
|
|
Xu W., Meiyun Z., Mingtao Z., He R.2010. Constraint bagging for stock price prediction using neural networks. In International Conference on Modelling, Identification and Control, Okayama, Japan, July 17–19, 606–610.
Google Scholar
|
|
Xu X., Zhang A.2006. Boost feature subset selection: a new gene selection algorithm for microarray data set. In International Conference on Computational Science, UK, 670–677.
Google Scholar
|
|
Yang L., Gong W., Gu X., Li W., Liu Y.2009. Bagging null space locality preserving discriminant classifiers for face recognition. Pattern Recognition42, 1853–1858.
Google Scholar
|
|
Yasumura Y., Kitani N., Uehara K.2005. Integration of bagging and boosting with a new reweighting technique. In International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC05), Vienna, Austria, 338–343.
Google Scholar
|
|
Yi X.-C., Ha Z., Liu C.-P.2004. Selective bagging based incremental learning. In Third International Conference on Machine Learning and Cyhemetics, Shanghai, August 26–29, 2412–2417.
Google Scholar
|
|
Yin H., Dong H.2011. The problem of noise in classification: Past, current and future work. In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN), May 27–29, 412–416.
Google Scholar
|
|
Yin X.-C., Liu C.-P., Zhi H.2005. Feature combination using boosting. Pattern Recognition Letters26, 2195–2205.
Google Scholar
|
|
Zaman F., Hirose H.2008. A robust bagging method using median as a combination rule. In IEEE 8th International Conference on Computer and Information Technology Workshops, Dhaka, Bangladesh, 55–60.
Google Scholar
|
|
Zhang C. X., Zhang J. S.2008a.A local boosting algorithm for solving classification problems. Computational Statistics & Data Analysis52(4), 1928–1941.
Google Scholar
|
|
Zhang C. X., Zhang J. S.2008b. RotBoost: a technique for combining Rotation Forest and AdaBoost. Pattern Recognition Letters29, 1524–1536.
Google Scholar
|
|
Zhang C. X., Zhang J. S., Zhang G.-Y.2008. An efficient modified boosting method for solving classification problems. Journal of Computational and Applied Mathematics214, 381–392.
Google Scholar
|
|
Zhang C. X., Zhang J. S., Zhang G.-Y.2009. Using boosting to prune double-bagging ensembles. Computational Statistics and Data Analysis53, 1218–1231.
Google Scholar
|
|
Zhang D., Zhou X., Leung S., Zheng J.2010. Vertical bagging decision trees model for credit scoring. Expert Systems with Applications37, 7838–7843.
Google Scholar
|
|
Zhou Z.-H., Wu J., Tang W.2002. Ensembling neural networks: many could be better than all. Artificial Intelligence137(1–2), 239–263.
Google Scholar
|
|
Zhou Z. H., Yu Y.2005a.Adapt bagging to nearest neighbor classiers. Journal of Computer Science and Technology20(1), 48–54.
Google Scholar
|
|
Zhou Z. H., Yu Y.2005b.Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics Part B:Cybernetics35(4), 725–735.
Google Scholar
|
|
Zhu X., Yang Y.2008. A lazy bagging approach to classification. Pattern Recognition41, 2980–2992.
Google Scholar
|
