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<p><b>Abstract</b>—Mutual information is a good indicator of relevance between variables, and have been used as a measure in several feature selection algorithms. However, calculating the mutual information is difficult, and the performance of a feature selection algorithm depends on the accuracy of the mutual information. In this paper, we propose a new method of calculating mutual information between input and class variables based on the Parzen window, and we apply this to a feature selection algorithm for classification problems.</p>
Feature selection, mutual information, Parzen window.
Chong-Ho Choi, Nojun Kwak, "Input Feature Selection by Mutual Information Based on Parzen Window", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. , pp. 1667-1671, December 2002, doi:10.1109/TPAMI.2002.1114861
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