Troika - An improved stacking schema for classification tasks

Information Sciences

Volumn 179, Issue 24, 2009, Pages 4097-4122

  Menahem, Eitan ^a   Rokach, Lior ^a   Elovici, Yuval ^a  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

Author keywords

Ensemble of classifiers; Machine learning; Meta combination; Stacked generalization

Indexed keywords

BASE CLASSIFIERS; CLASSIFICATION TASKS; COMBINING CLASSIFIERS; DATA SETS; ENSEMBLE CLASSIFIERS; ENSEMBLE OF CLASSIFIERS; GENERAL ENSEMBLE METHOD; MACHINE LEARNING; META COMBINATION; META-CLASSIFIERS; META-LEARNING APPROACH; MULTI-CLASS PROBLEMS; STACKED GENERALIZATION; STACKING METHOD; THREE-LAYER;

ROBOT LEARNING;

CLASSIFIERS;

EID: 70349750474     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2009.08.025     Document Type: Article

References (33)

1. Anand R., Methrotra K., Mohan C.K., and Ranka S. Efficient classification for multiclass problems using modular neural networks. IEEE Transaction on Neural Networks 6 1 (1995) 117-125
2. Aydi{dotless}n U., Murat S., Yi{dotless}ldi{dotless}z O.T., and Ethem A. Incremental construction of classifier and discriminant ensembles. Information Science 179 9 (2009) 1298-1318
3. B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Fifth Annual ACM, ACM Press, Pittsburgh, PA, 1992, pp. 144-152.
4. W. Buntine, A theory of learning classification rules, Doctoral dissertation, School of Computing Science, University of Technology, Sydney, 1990.
5. Braha D., Elovici Y., and Last M. A theory of actionable data mining with application to semiconductor manufacturing control. International Journal of Production Research 45 13 (2007) 3059-3084
6. Breiman L. Bagging predictors. Machine Learning (1996) 123-140
7. P. Clark, R. Boswell, Rule induction with CN2: some recent improvements, in: Proceedings of the European Working Session on Learning, 1991, pp. 151-163.
8. Cohen S., Rokach L., and Maimon O. Decision tree instance space decomposition with grouped gain-ratio. Information Science 177 17 (2007) 3592-3612
9. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006) 1-30
10. Dietterich T.G., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2 (1995) 263-286
11. Dzeroski S., and Zenko B. Is combining classifiers with stacking better than selecting the best one?. Machine Learning 54 3 (2004) 255-273
12. Provost F., and Fawcett T. Robust classification for imprecise environments. Machine Learning 42 (2001) 203-231
13. J. Fürnkranz, Pairwise classification as an ensemble technique, in: European Conference on Machine Learning, Austrian Research Institute for Artificial Intelligence, Helsinki, Finland, 2002, pp. 97-110.
14. Peter Zhang G. A neural network ensemble method with jittered training data for time series forecasting. Information Sciences: An International Journal 177 23 (2007) 5329-5346
15. G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338-345.
16. Lorena A., and de Carvalho A.C.P.L.F. Evolutionary Design of Code-Matrices for Multiclass Problems, Soft Computing for Knowledge Discovery and Data Mining (2007), Springer, US pp. 153-184
17. Lu B.L., and Ito M. Task decomposition and module combination based on class relations: a modular neural network for pattern classification. IEEE Transactions on Neural Networks 10 5 (1999) 1244-1256
18. Menahem E., Shabtai A., Rokach L., and Elovici Y. Improving malware detection by applying multi-inducer ensemble. Computational Statistics & Data Analysis 53 4 (2009) 1483-1494
19. C.J. Merz, P.M. Murphy, UCI repository of machine learning databases, University of California, Department of Information and Computer Science, Irvine, CA, 1998.
20. Merz C. Using correspondence analysis to combine classifiers. Machine Learning 36 (1999) 33-58
21. Quinlan R. C4.5: programs for machine learning. Machine Learning (1993) 235-240
22. Rokach L. Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography. Computational Statistics and Data Analysis 53 12 (2009) 4046-4072
23. A.K. Seewald, J. Fuernkranz, An evaluation of grading classifiers, in: Advances in Intelligent Data Analysis: Fourth International Conference, Springer, Berlin/Heidelberg/New York/Tokyo, 2001, pp. 115-124.
24. A.K. Seewald, Towards understanding stacking - studies of a general ensemble learning scheme, PhD-Thesis, TU Wien, 2003.
25. A. Seewald, How to make stacking better and faster while also taking care of an unknown weakness, in: Nineteenth International Conference on Machine Learning, Morgan Kaufmann Publishers, Sydney, 2002, pp. 554-561.
26. Sivalingam D., Pandian N., and Ben-Arie J. Minimal classification method with error-correcting codes for multiclass recognition. International Journal of Pattern Recognition and Artificial Intelligence 19 5 (2005) 663-680
27. Ting K.M., and Witten I.H. Issues in stacked generalization. Journal of Artificial Intelligence Research 10 (1999) 271-289
28. Opitz D., and Shavlik J. Generating accurate and diverse members of a neural network ensemble. Advances in Neural Information Processing Systems 8 (1996) 535-541
29. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques. second ed. (2005), Morgan Kaufmann, San Francisco
30. Wolpert D. Stacked generalization. Neural Networks 5 (1992) 241-259
31. B.E. Boser, l.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, PA, 1992, pp. 144-152.
32. Zhoua J., Pengb H., and Suenc C. Data-driven decomposition for multi-class classification. Pattern Recognition 41 (2008) 67-76
33. G. Zenobi, P. Cunningham, Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error, in: Proceedings of the European Conference on Machine Learning, 2001.