Abstract
Feature selection is an important preprocessing step in pattern classification and machine learning, and mutual information is widely used to measure relevance between features and decision. However, it is difficult to directly calculate relevance between continuous or fuzzy features using mutual information. In this paper we introduce the fuzzy information entropy and fuzzy mutual information for computing relevance between numerical or fuzzy features and decision. The relationship between fuzzy information entropy and differential entropy is also discussed. Moreover, we combine fuzzy mutual information with ”min-Redundancy-Max-Relevance”, ”Max-Dependency” and ”min-Redundancy-Max-Dependency” algorithms. The performance and stability of the proposed algorithms are tested on benchmark data sets. Experimental results show the proposed algorithms are effective and stable.
Highlights
As the capability of acquiring and storing information increases, more and more candidate features are gathered in pattern recognition and machine learning
We can evaluate the performance of feature selection algorithms with the size and classification performance of selected features [22,32,33]
We evaluate stability of feature selection algorithms with the similarity of feature rankings and that of feature subsets
Summary
As the capability of acquiring and storing information increases, more and more candidate features are gathered in pattern recognition and machine learning. Most of these features are usually irrelevant or redundant for a given learning task. A great number of feature selection algorithms based on mutual information have been developed in recent years [1,7,11,18,19,20,24,37,40,42]. In constructing a feature selection algorithm, there are two key issues: evaluation measure and search strategy. A number of measures have been developed so far, such as dependency [28,41,46], consistency [6,32], fuzzy dependency 12 and mutual information 1
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