Feature selection by Differential Evolution algorithm - A case study in personnel identification

Feature selection is an important area of research as it has a tremendous effect on the accuracy and performance of classification algorithms. In this paper we propose an objective function for feature selection, which combines the intra class feature variation and inter class feature distance using a Lagrangian multiplier. The inter class distance is measured using the sum of absolute difference of the ratio of mean and standard deviation for respective classes. The objective function is minimized using Differential Evolutionary (DE) Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM) is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton datapoints obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.