Combination methods

Contains

I. For label outputs Majority vote BKS (Behaviour Knowledge Space) Naïve Bayes

II. For continuous-valued outputs Average Median Minimum Maximum Product Weighted average Ridge regression Decision template LDC combiner Tree combiner

GitHub links: Combination methods for classifier ensembles

References

• Kuncheva L. I. and J. J. Rodriguez, A weighted voting framework for classifiers ensembles, Knowledge and Information Systems 38, 2014, 259-275 (DOI: 10.1007/s10115-012-0586-6). pdf [bib]
• Kuncheva L.I. Combining Pattern Classifiers. Methods and Algorithms, Wiley, Second edition, 2014.
• Kuncheva L.I. Fuzzy vs Non-fuzzy in combining classifiers designed by boosting, IEEE Transactions on Fuzzy Systems, 11 (6), 2003, 729-741. pdf
*** Won the best paper award for 2006 in IEEE Transactions on Fuzzy Systems.
• Kuncheva L.I., C.J. Whitaker, C.A. Shipp, R.P.W. Duin. Limits on the majority vote accuracy in classifier fusion, Pattern Analysis and Applications, 6, 2003, 22-31. pdf
• Kuncheva L.I. A theoretical study on six classifier fusion strategies, IEEE Transactions on PAMI, 24, (2), 2002, 281-286. pdf
• Kuncheva L.I. Combining classifiers: Soft computing solutions, in: S.K. Pal and A. Pal (Eds.) Pattern Recognition: From Classical to Modern Approaches, World Scientific Publishing Co., Singapore, 2001, 427-452. pdf