Abstract
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.
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Brown, G. (2009). An Information Theoretic Perspective on Multiple Classifier Systems. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_35
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DOI: https://doi.org/10.1007/978-3-642-02326-2_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
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