LibD3C: Ensemble classifiers with a clustering and dynamic selection strategy

Neurocomputing

Volumn 123, Issue , 2014, Pages 424-435

  Lin, Chen ^a   Chen, Wenqiang ^a   Qiu, Cheng ^a   Wu, Yunfeng ^a   Krishnan, Sridhar ^b   Zou, Quan ^a  

^a XIAMEN UNIVERSITY   (China)

^b RYERSON UNIVERSITY   (Canada)

Author keywords

Circulating combination; Clustering; Dynamic selection; Machine learning; Multi label classification; Selective ensemble learning

Indexed keywords

CIRCULATING COMBINATION; CLUSTERING; DYNAMIC SELECTION; MULTI-LABEL CLASSIFICATIONS; SELECTIVE ENSEMBLES;

LEARNING SYSTEMS; NEURAL NETWORKS;

COMPUTER APPLICATIONS;

ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; CLUSTER ANALYSIS; COMPUTER ANALYSIS; COMPUTER PROGRAM; CONCEPTUAL FRAMEWORK; DATA ANALYSIS; LEARNING ALGORITHM; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; PROCESS CONTROL; PROCESS DESIGN; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; PROCESS TECHNOLOGY; SELECTIVE ENSEMBLE ALGORITHM;

EID: 84885838906     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.08.004     Document Type: Article

References (41)

1. Breiman L. Bagging predictors. Machine Learning 1996, 24(2):123-140.
2. Y. Freund, R.E. Schapire, Experiments with a new boosting algorithm, in: Proceedings of the International Workshop then Conference in Machine Learning, Morgan Kaufmann Publishers, Inc., 1996, pp. 148-156.
3. Breiman L. Bias, Variance, and Arcing Classifiers 1996, Statistics Department, University of California, Berkeley, CA, (Technical Report 460).
4. Zhang Y., et al. Prediction of novel pre-microRNAs with high accuracy through boosting and SVM. Bioinformatics 2011, 27(10):1436-1437.
5. Quan Z., et al. A classification method for class imbalanced data and its application on bioinformatics. Journal of Computer Research and Development 2010, 47(8):1407-1414.
6. Jiang Y., et al. Mining extremely small data sets with application to software reuse. Software: Practice and Experience 2009, 39(4):423-440.
7. Geng X., Zhou Z.-H. Image region selection and ensemble for face recognition. Journal of Computer Science and Technology 2006, 21(1):116-125.
8. Zhou Z.-H., et al. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 2002, 24(1):25-36.
9. A. Lazarevic, Z. Obradovic, Effective pruning of neural network classifier ensembles, in: Proceedings of the IEEE International Joint Conference Neural Networks, IJCNN, 2001, pp. 796-801.
10. D.D. Margineantu, T.G. Dietterich, Pruning adaptive boosting, in: Proceedings of the International Workshop then Conference in Machine Learning, Citeseer, 1997, pp. 211-218.
11. Quinlan J.R. C4.5: Programs for Machine Learning 1993, Morgan Kaufmann, San Mateo, CA.
12. Zhou Z.-H., et al. Ensembling neural networks: many could be better than all. Artificial Intelligence 2002, 137(1):239-263.
13. Kaufman L., Rousseeuw P.J. Finding Groups in Data: An Introduction toCluster Analysis 2009, Wiley-Interscience, Hoboken, NJ.
14. Foli R., Giacinto G. An approach to the automatic design of multiple classifier system. Pattern Recognition Letters 2001, 22(1):25-33.
15. Bryll R., et al. Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recognition 2003, 36(6):1291-1302.
16. Croux C., et al. Trimmed bagging. Computational Statistics and Data Analysis 2007, 52(1):362-368.
17. L. Cheng, et al., Sampled-data based average consensus of second-order integral multi-agent systems: switching topologies and communication noises, Automatica, 49 (5) (2013) 1458-1464.
18. Tsoumakas G., Katakis I. Multi-label classification: an overview. International Journal of Data Warehousing and Mining (IJDWM) 2007, 3(3):1-13.
19. R. Yan, et al., Model-shared subspace boosting for multi-label classification, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2007, pp. 834-843.
20. Schapire R.E., Singer Y. BoosTexter: a boosting-based system for text categorization. Machine Learning 2000, 39(2-3):135-168.
21. Freund Y., Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 1997, 55(1):119-139.
22. De Comité F., et al. Learning Multi-label Alternating Decision Trees from Texts and Data. Machine Learning and Data Mining in Pattern Recognition 2003, 35-49. Springer, Berlin, Germany.
23. J. Read, et al., Multi-label classification using ensembles of pruned sets, in: Proceedings of the Eighth IEEE International Conference on Data Mining, ICDM, 2008, pp. 995-1000.
24. Zhang J., Chau K.-W. Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. Journal of Universal Computer Science 2009, 15(4):840-858.
25. Kuncheva L.I., Whitaker C.J. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 2003, 51(2):181-207.
26. Hao H.-W., et al. Dynamic selection and circulating combination for multiple classifier systems. Acta Automatica Sinica 2011, 37(11):1290-1295.
27. H. Hao, et al., Comparison of genetic algorithm and sequential search methods for classifier subset selection, in: Proceedings of the Seventh International Conference on Document Analysis and Recognition, Citeseer, 2003, pp. 765.
28. Tsoumakas G., et al. Random k-label sets for multilabel classification. IEEE Transactions on Knowledge and Data Engineering 2011, 23(7):1079-1089.
29. Tsoumakas G., et al. Mining Multi-label Data. Data Mining and Knowledge Discovery Handbook 2010, 667-685. Springer, New York, NY.
30. C.-W. Hsu, et al., A practical guide to support vector classification, 2003.
31. C. Blake and C.J. Merz, UCI repository of machine learning databases, 1998.
32. Zhou Z.-H., Jiang Y. NeC4.5: neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering 2004, 16(6):770-773.
33. L. Cheng, et al., Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks, IEEE Transactions on Neural Networks, 21(5) (2011) 714-726.
34. H. Homburg, et al., A benchmark dataset for audio classification and clustering, in: Proceedings of the International Conference on Music Information Retrieval, 2005, pp. 528-531.
35. Z. Hou, et al., Multi-criteria optimization for coordination of redundant robots using a dual neural network, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40 (4) (2010) 1075-1087.
36. K.T.G. Tsoumakas, et al., Multi-label classification of music into emotions, in: Proceedings of the 9th International Conference of Music Information Retrieval, ISMIR, Lulu.com, 2008, pp. 325.
37. Zhang M.-L., Zhou Z.-H. Multilabel neural networks with applications to functional genomics and text categorization. IEEE Transactions on Knowledge and Data Engineering 2006, 18(10):1338-1351.
38. Platt J.C. Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning 1998, MIT Press, Cambridge, MA. B. Sch olkopf, C.J.C. Burges, A.J. Smola (Eds.).
39. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
40. K. Aryafar, A. Shokoufandeh, Music genre classification using explicit semantic analysis, in: Proceedings of the 1st International ACM Workshop on Music Information Retrieval with User-Centered and Multimodal Strategies, ACM, 2011, pp. 33-38.
41. Zhang M.-L., Zhou Z.-H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognition 2007, 40(7):2038-2048.