Abstract
Feature selection is an important task which can affect the performance of pattern classification and recognition system. This study uses two nature-inspired algorithms, namely genetic algorithms and particle swarm optimization, for this problem. The algorithms adopt classifier performance and the number of the selected features as heuristic information, and select the optimal feature subset in terms of feature set size and classification performance. From experimental results, the major contribution of this work are: the reduction of vector feature size without loosing performance which is crucial for real time application and low-resources devices as well as the time needed to find the optimal subset features compared to exhaustive search or other conventional methods (less than 50 iterations instead of billions of iterations to test all possible configurations).
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Harrag, A. Nature-inspired feature subset selection application to arabic speaker recognition system. Int J Speech Technol 18, 245–255 (2015). https://doi.org/10.1007/s10772-014-9264-2
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DOI: https://doi.org/10.1007/s10772-014-9264-2