A Differential Evolution Approach to Dimensionality Reduction for Classification Needs (CROSBI ID 198787)
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Podaci o odgovornosti
Martinović, Goran ; Bajer, Dražen ; Zorić, Bruno
engleski
A Differential Evolution Approach to Dimensionality Reduction for Classification Needs
The feature selection problem often occurs in pattern recognition, and more specific, classification. Although these patterns could contain a large number of features, some of them could prove to be irrelevant, redundant or even detrimental to classification accuracy. Thus, it is important to remove these kinds of features which in turn leads to problem dimensionality reduction and could eventually improve the classification accuracy. In this paper an approach to dimensionality reduction based on differential evolution which represents a wrapper and explores the solution space is presented. The solutions, subsets of the whole feature set, are evaluated using the k-nearest neighbour algorithm. High quality solutions found during execution of the differential evolution fill the archive. A final solution is obtained by conducting k-fold cross validation on the archive solutions and selecting the best. Experimental analysis was conducted on several standard test sets. Classification accuracy of the k-nearest neighbour algorithm using the full feature set and the accuracy of the same algorithm using only the subset provided by the proposed approach and some other optimization algorithms which were used as wrappers are compared. The analysis has shown that the proposed approach successfully determines good feature subsets which may increase classification accuracy.
classification; differential evolution; feature subset selection; k-nearest neighbour algorithm; wrapper method
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Podaci o izdanju
24 (1)
2014.
111-122
objavljeno
1641-876X
10.2478/amcs-2014-0009