Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Robust mixture modelling using the t distribution
Statistics and Computing
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Information Theory and Statistical Learning
Information Theory and Statistical Learning
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Mutual information and minimum mean-square error in Gaussian channels
IEEE Transactions on Information Theory
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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Feature selection is an important preprocessing step for many high-dimensional regression problems. One of the most common strategies is to select a relevant feature subset based on the mutual information criterion. However, no connection has been established yet between the use of mutual information and a regression error criterion in the machine learning literature. This is obviously an important lack, since minimising such a criterion is eventually the objective one is interested in. This paper demonstrates that under some reasonable assumptions, features selected with the mutual information criterion are the ones minimising the mean squared error and the mean absolute error. On the contrary, it is also shown that the mutual information criterion can fail in selecting optimal features in some situations that we characterise. The theoretical developments presented in this work are expected to lead in practice to a critical and efficient use of the mutual information for feature selection.