Conference Proceedings

Reconsidering Mutual Information Based Feature Selection: A Statistical Significance View

Xuan Vinh Nguyen, Jeffrey Chan, James Bailey

Proceedings of the National Conference on Artificial Intelligence | ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE | Published : 2014

Abstract

Copyright © 2014, Association for the Advancement of Artificial Intelligence. Mutual information (MI) based approaches are a popular feature selection paradigm. Although the stated goal of Mi-based feature selection is to identify a subset of features that share the highest mutual information with the class variable, most current Mi-based techniques are greedy methods that make use of low dimensional MI quantities. The reason for using low dimensional approximation has been mostly attributed to the difficulty associated with estimating the high dimensional MI from limited samples. In this paper, we argue a different viewpoint that, given a very large amount of data, the high dimensional MI o..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

This work is supported by the Australian Research Council via grant number FT110100112.