Evolutionary local improvement on genetic algorithms for feature selection
Leandro D. Vignolo, Matias F. Gerard · 2017 XLIII Latin American Computer Conference (CLEI) · 2017
Feature selection is an extremely important matter in pattern recognition, particularly when a large set of features is available without knowledge about the discriminative information provided by each element. The key issue is to define a criterion in order to rank the features, discarding those features that are less relevant, redundant, or noisy. This depends on the particular task, the classifier and the properties of the data. A frequent approach consists on the use of genetic algorithms guided by the classification accuracy. However they are often not able to provide a solution with both a considerable reduction of dimensionality and high accuracy rate. Here we propose a modified version of a genetic algorithm, introducing a novel local improvement approach based on evolution, which is able to obtain better dimensionality-accuracy trade-off. Experimental results on different well known datasets show the advantages of our proposal.