Abstract
In this paper, we introduce a new diversity measure for classifier combining, called Entropy-based Pair-wise Diversity Measure (EBPDM). Its application to help removing redundant classifiers from a face classifier ensemble is conducted. The preliminary experiments on UC Irvine repository and AT&T face database demonstrate that, compared with other diversity measures, the proposed measure is comparable at predicting the performance of multiple classifier systems, and is able to make classifier ensembles smaller without loss in performance.
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Liu, W., Wu, Z., Pan, G. (2004). An Entropy-Based Diversity Measure for Classifier Combining and Its Application to Face Classifier Ensemble Thinning. In: Li, S.Z., Lai, J., Tan, T., Feng, G., Wang, Y. (eds) Advances in Biometric Person Authentication. SINOBIOMETRICS 2004. Lecture Notes in Computer Science, vol 3338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30548-4_14
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DOI: https://doi.org/10.1007/978-3-540-30548-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24029-7
Online ISBN: 978-3-540-30548-4
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