Looking inside self-organizing map ensembles with resampling and negative correlation learning

Neural Networks

Volumn 24, Issue 1, 2011, Pages 130-141

  Scherbart, Alexandra ^a   Nattkemper, Tim W ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

Bagging; Ensemble learning; Negative correlation learning; Random subspace method; Regression; Self organizing maps

Indexed keywords

BAGGING; ENSEMBLE LEARNING; NEGATIVE CORRELATION LEARNING; RANDOM SUBSPACE METHOD; REGRESSION;

CONFORMAL MAPPING; CORRELATION METHODS;

SELF ORGANIZING MAPS;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; CORRELATION ANALYSIS; MACHINE LEARNING; PREDICTION; PRIORITY JOURNAL; SELF ORGANIZING MAP; STATISTICAL ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEEDBACK; HUMANS; LEARNING; PATTERN RECOGNITION, AUTOMATED;

EID: 78649679693     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2010.08.004     Document Type: Article

References (38)

1. Asuncion, A., & Newman, D. J. (2007). UCI machine learning repository.
2. Breiman, L. (2001). Random forests. In Machine learning (pp. 5-32).
3. Breiman, L. (1996). Bagging predictors. In Machine learning (pp. 123-140).
4. Brown, G. (2004). Diversity in neural network ensembles. Ph.D. thesis. University of Birmingham.
5. Brown G., Wyatt J., Harris R., Yao X. Diversity creation methods: a survey and categorisation. Journal of Information Fusion 2005, 6:5-20.
6. Brown G., Wyatt J., Sun P. Between two extremes: examining decompositions of the ensemble objective function. LNCS 2005, Vol. 3541.
7. Brown G., Wyatt J.L., Kaelbling P. Managing diversity in regression ensembles. Journal of Machine Learning Research 2005, 6.
8. Cleveland W.S., Devlin S.J. Locally-weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association 1988, 83:596-610.
9. Cortez, P., & Morais, A. (2007). A data mining approach to predict forest fires using meteorological data. In J. Neves, M. F. Santos & J. Machado (Eds.) New trends in artificial intelligence, proceedings of the 13th EPIA 2007-Portuguese conference on artificial intelligence (pp. 512-523.
10. Dietterich T.G. The handbook of brain theory and neural networks. Ensemble learning 2002, 405-408. The MIT Press, Cambridge, MA, (Chapter). second ed. M.A. Arbib (Ed.).
11. Dimitriadou, E., Hornik, K., Leisch, F., Meyer, D., & Weingessel, A. (2009). E1071: misc functions of the department of statistics (e1071). TU Wien. R package version 1.5-19.
12. Dittenbach M., Merkl D., Rauber A. The growing hierarchical self-organizing map. IJCNN (6) 2000, 15-19. IEEE Computer Society.
13. Freund Y., Schapire R.E. Experiments with a new boosting algorithm. Proceedings of the thirteenth international conference on machine learning 1996, 148-156. Morgan Kaufmann.
14. Friedman J.H. Multivariate adaptive regression splines. The Annals of Statistics 1991, 19:1-67.
15. Fritzke B. Fast learning with incremental RBF networks. Neural Processing Letters 1994, 2-5.
16. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Computation 1992, 4:1-58.
17. Goerke N., Kintzler F., Eckmiller R. Self organized partitioning of chaotic attractors for control. ICANN'01: proceedings of the international conference on artificial neural networks 2001, 851-856. Springer-Verlag, London, UK.
18. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction 2001, Springer.
19. Ho T.K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998, 20:832-844.
20. Inoue H., Narihisa H. Effective pruning method for a multiple classifier system based on self-generating neural networks. Artificial neural networks and neural information processing-ICANN/ICONIP 2003 2003, Vol. 2714:11-18. Springer, Berlin, Heidelberg.
21. Kawashima S., Ogata H., Kanehisa M. AAindex: amino acid index database. Nucleic Acids Research 1999, 27:368-369.
22. Kohonen T. Self-organized formation of topologically correct feature maps. Biological Cybernetics, Vol. 43 1982, 59-69.
23. Krogh A., Vedelsby J. Neural network ensembles, cross validation, and active learning. Adv. in NIPS 1995, 231-238. MIT Press.
24. Liaw A., Wiener M. Classification and regression by randomforest. R News 2002, 2:18-22.
25. Liu Y., Yao X. Ensemble learning via negative correlation. Neurocomputing 1999, 12:1399-1404.
26. Miikkulainen R. Script recognition with hierarchical feature maps. Connection Science 1990, 2:83-101.
27. Millington, P. J., & Baker, W. L. (1990). Associative reinforcement learning for optimal control. In Proc. conf. on AIAA guid. nav. and cont. Vol. 2 (pp. 1120-1128).
28. Minku F.L., Inoue H., Yao X. Negative correlation in incremental learning. Natural Computing 2009, 8:289-320.
29. Prudhomme, E., & Lallich, S. (2008). Optimization of self-organizing maps ensemble in prediction. In International conference on data mining. DMIN'08.
30. R Development Core Team (2008). R: a language and environment for statistical computing. R Foundation for Stat. Comp. Austria. ISBN: 3-900051-07-0.
31. Ritter H. Learning with the self-organizing map. Artificial neural networks 1991, 379-384. Elsevier Science Publishers. T. Kohonen (Ed.).
32. Scherbart, A. (2009). Lerranco: SOM ensembles. R package version 0.1.
33. Scherbart A., Nattkemper T.W. The diversity of regression ensembles combining bagging and random subspace method. Lecture notes in computer science 2008, Vol. 5507:911-918. Springer. M. Köppen, N.K. Kasabov, G.G. Coghill (Eds.).
34. Scherbart, A., Timm, W., Böcker, S., & Nattkemper, T. W. (2007). Som-based peptide prototyping for mass spectrometry peak intensity prediction. In WSOM'07.
35. Schölkopf B., Bartlett P., Smola A., Williamson R. Shrinking the tube: a new support vector regression algorithm. Advances in Neural Information Processing Systems 1999.
36. Timm W., Scherbart A., Böcker S., Kohlbacher O., Nattkemper T.W. Peak intensity prediction in MALDI-TOF mass spectrometry: a machine learning study to support quantitative proteomics. BMC Bioinformatics 2008, 9:443.
37. Vlachos, P. (2005). Statlib datasets archive.
38. Wehrens, R., & Mevik, B. -H. (2007). PLS: Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR). R package version 2.1-0.