From dynamic classifier selection to dynamic ensemble selection. (English) Zbl 1140.68466

Summary: In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.


68T10 Pattern recognition, speech recognition


Full Text: DOI


[1] Brown, G.; Wyatt, J.; Harris, R.; Yao, X., Diversity creation methods: a survey and categorisation, Int. J. Inf. Fusion, 6, 1, 5-20 (2005)
[2] Kittler, J.; Hatef, M.; Duin, R.; Matas, J., On combining classifiers, IEEE Trans. Pattern Anal. Mach. Intell., 20, 3, 226-239 (1998)
[3] Kuncheva, L. I.; Whitaker, C. J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Mach. Learn., 51, 2, 181-207 (2003) · Zbl 1027.68113
[4] Ho, T. K., The random space method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell., 20, 8, 832-844 (1998)
[6] Kuncheva, L. I.; Skurichina, M.; Duin, R. P.W., An experimental study on diversity for Bagging and Boosting with linear classifiers, Int. J. Inf. Fusion, 3, 2, 245-258 (2002)
[7] Schapire, R. E.; Freund, Y.; Bartlett, P.; Lee, W. S., Boosting the margin: a new explanation for the effectiveness of voting methods, Ann. Stat., 26, 5, 1651-1686 (1998) · Zbl 0929.62069
[9] Cao, J.; Ahmadi, M.; Shridhar, M., Recognition of handwritten numerals with multiple feature and multistage classifier, Pattern Recognition, 28, 2, 153-160 (1995)
[10] Didaci, L.; Giacinto, G.; Roli, F.; Marcialis, G. L., A study on the performances of dynamic classifier selection based on local accuracy estimation, Pattern Recognition, 38, 11, 2188-2191 (2005) · Zbl 1077.68797
[13] Gunes, V.; Ménard, M.; Loonis, P.; Petit-Renaud, S., Combination, cooperation and selection of classifiers: a state of the art, Int. J. Pattern Recognition Artif. Intell., 17, 8, 1303-1324 (2003)
[14] Kuncheva, L. I., Switching between selection and fusion in combining classifiers: an experiment, IEEE Trans. Syst. Man Cybern. Part B, 32, 2, 146-156 (2002)
[15] Woods, K.; Kegelmeyer, W. P.; Bowyer, K., Combination of multiple classifiers using local accuracy estimates, IEEE Trans. Pattern Anal. Mach. Intell., 19, 4, 405-410 (1997)
[21] Oliveira, L. S.; Sabourin, R.; Bortolozzi, F.; Suen, C. Y., Automatic recognition of handwritten numerical strings: a recognition and verification strategy, IEEE Trans. Pattern Anal. Mach. Intell., 24, 11, 1438-1454 (2002)
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