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XiVj^/A/-)}. The relationship between a pair of classifiers Ci and Cj can be shown in the form of the 2 x 2 contingency table (Table 1). In order to use this table for any number of classes the "oracle" labels are applied. e. it recognizes the true class (y^) of the example x^, and Ri = 0 means that the classifier is wrong. Classifiers Rj = l Rj = o a b Ri = l c d Ri = 0 Table 1. A 2 x 2 contingency table for the "oracle" outputs Kuncheva et al. (2000) proposed the Yule's Q statistics to evaluate the diversity of all possible component classifier pairs.

1997): An introduction to recursive partitioning using the RPART routines. Mayo Foundation, Rochester. TUMER, K. and GHOSH, J. (1996): Analysis of decision boundaries in linearly combined neural classifiers. Pattern Recognition, 29, 341-348. WOLPERT, D. (1992): Stacked generalization. Neural Networks 5, 241-259. ZENOBI, G. and CUNNINGHAM, P. (2001): Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. Lecture Notes in Computer Science 2167, 576-587.

DIDAY, E. H. Bock and E. ): Analysis of Symbolic Data. Springer, Heidelberg, 1-23. , and WINSBERG, S. (2003): Symbolic class description with interval data. Journal of Symbolic Data Analysis, 1. ] PERINEL, E. (1999): Construire un arbre de discrimination binaire a partir de donnees imprecises. Revue de Statistique Appliquee, J^l, 5-30. R. (1986): Induction of decision trees. Machine Learning, 1, 81-106. , and WINSBERG, S. (2002): Symbolic class description In: K. Jajuga, A. H. ): Classification, Clustering, and Data Analysis.

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A nonparametric approach to the analysis of longitudinal data via a set of level crossing problems w by Reilly C.

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