Summary
Assuring diversity of classifiers in an ensemble plays a crucial role in the multiple classifier system design. The paper presents a comparative study of selected methods which can assure the diversity by manipulating the individual classifier inputs i.e., they train learner using subspaces of a feature set or they try to exploit local competencies of individual classifier for a given subset of feature space. This work is a starting point for developing new methods of diversity assurance embedded in a multiple classifier system design. All methods had been evaluated on the basis of computer experiments which were carried out on benchmark datasets. On the basis of received results conclusions about the usefulness of examined methods for certain types of problems were drawn.
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References
Alpaydin, E.: Combined 5 x 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cordella, L.P., Foggia, P., Sansone, C., Tortorella, F., Vento, M.: A Cascaded Multiple Expert System for Verification. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 330–339. Springer, Heidelberg (2000)
Frank, A., Asuncion, A.: UCI machine learning repository (2010), http://archive.ics.uci.edu/ml
Giacinto, G.: Design multiple classifier systems. Technical Report PhD thesis, Universita Degli Studi di Salerno, Salerno, Italy (1998)
Giacinto, G., Roli, F., Fumera, G.: Design of effective multiple classifier systems by clustering of classifiers. In: Proceedings of 15th International Conference on Pattern Recognition, vol. 2, pp. 160–163 (2000)
Ho, K.T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998)
Holmes, G., Donkin, A., Witten, I.H.: Weka: A machine learning workbench. In: Proceedings of Australian and New Zealand Conference on Intelligent Information Systems, pp. 357–361 (1994)
Hornik, K., Buchta, C., Zeileis, A.: Open-source machine learning: R meets weka. Computational Statistics 24(2), 225–232 (2009)
Jackowski, K., Wozniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Analysis and Applications 12(4), 415–425 (2009)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Krawczyk, B.: Classifier Committee Based on Feature Selection Method for Obstructive Nephropathy Diagnosis. In: Katarzyniak, R., Chiu, T.-F., Hong, C.-F., Nguyen, N.T. (eds.) Semantic Methods for Knowledge Management and Communication. SCI, vol. 381, pp. 115–125. Springer, Heidelberg (2011)
Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 185–188 (2000)
Partridge, D., Krzanowski, W.: Software diversity: practical statistics for its measurement and exploitation. Information and Software Technology 39(10), 707–717 (1997)
Rastrigin, L.A., Erenstein, R.H.: Method of Collective Recognition. Energoizdat, Moscow (1981)
Rodríguez, J.J., Kuncheva, L.I., Alonso, C.J.: Rotation forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1619–1630 (2006)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005)
R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2008)
Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Proc. 6th Online World Conference on Soft Computing in Industrial Applications, pp. 25–42 (2001)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Proceedings of Twentieth International Conference on Machine Learning, vol. 2, pp. 856–863 (2003)
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Krawczyk, B., Woźniak, M. (2013). Analysis of Diversity Assurance Methods for Combined Classifiers. In: Choraś, R. (eds) Image Processing and Communications Challenges 4. Advances in Intelligent Systems and Computing, vol 184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32384-3_22
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DOI: https://doi.org/10.1007/978-3-642-32384-3_22
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