Input feature selection by mutual information based on Parzen window
Nojun Kwak, Chong‐Ho Choi · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2002
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.