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Input feature selection by mutual information based on Parzen window

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2 Author(s)
Kwak, Nojun ; Sch. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., South Korea ; Chong-Ho Choi

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.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:24 ,  Issue: 12 )