A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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A decision-theoretic generalization of on-line learning and an application to boosting
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This paper examines the benefits that information theory can bring to the study of multiple classifier systems. We discuss relationships between the mutual information and the classification error of a predictor. We proceed to discuss how this concerns ensemble systems, by showing a natural expansion of the ensemble mutual information into "accuracy" and "diversity" components. This natural derivation of a diversity term is an alternative to previous attempts to artificially define a term. The main finding is that diversity in fact exists at multiple orders of correlation, and pairwise diversity can capture only the low order components.