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Reports (Research Report) Year : 2003

Binary Feature Selection with Conditional Mutual Information

Abstract

In a context of classification, we propose to use conditional mutual information to select a family of binary features which are individually discriminating and weakly dependent. We show that on a task of image classification, despite its simplicity, a naive Bayesian classifier based on features selected with this Conditional Mutual Information Maximization (CMIM) criterion performs as well as a classifier built with AdaBoost. We also show that this classification method is more robust than boosting when trained on a noisy data set.
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Dates and versions

inria-00071638 , version 1 (23-05-2006)

Identifiers

  • HAL Id : inria-00071638 , version 1

Cite

François Fleuret. Binary Feature Selection with Conditional Mutual Information. [Research Report] RR-4941, INRIA. 2003. ⟨inria-00071638⟩
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