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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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https://hal.inria.fr/inria-00071638
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Submitted on : Tuesday, May 23, 2006 - 6:22:42 PM
Last modification on : Thursday, February 3, 2022 - 11:14:13 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:30:26 PM

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  • HAL Id : inria-00071638, version 1

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

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