A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
A novel feature selection approach by hybrid genetic algorithm
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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A method is proposed for automatic extraction of effective features for class separability. It applies to nonstationary processes described only by sample sets of stochastic signals. The extraction is based on time-frequency representations (TFRs) that are potentially suited to the characterization of nonstationarities. The features are defined by parameterized mappings applied to a TFR. These mappings select a region of the time-frequency plane by using a two-dimensional (2-D) parameterized weighting function and provide a standard characteristic in the restricted representation obtained. The features are automatically drawn from the TFR by tuning the weighting function parameters. The extraction is driven to maximize the information brought by the features about the class membership. It uses a mutual information criterion, based on estimated probability distributions. The framework is developed for the extraction of a single feature and extended to several features. A classification scheme adapted to the extracted features is proposed. Finally, some experimental results are given to demonstrate the efficacy of the method