Extreme Learning Machine-based Differential Evolution Feature Selection Approach for Feature Subset Selection from Digital Images

Abstract

In this paper, feature subset selection from river-ice image samples for classification of river-ice types is proposed using extreme learning machine (ELM) and differential evolution feature selection (DEFS) technique called extreme learning machine-based differential evolution feature selection (ELM-DEFS). Although DEFS is a good feature selection technique, the feature weights are selected by cross over and mutation which may be trapped. So, ELM is integrated with DEFS to overcome the difficulties of selecting the weights. Feature subset selection plays the most important role to classify the river-ice types. First-and second-order statistical texture features are extracted from real river-ice images. All the original features extracted from feature extraction methods do not help in recognition of river-ice types, so it is essential to reduce the feature subset to improve classification accuracy. In this paper, experiments are conducted on real river-ice image samples with probabilistic neural network (PNN) classifier which classifies different ice types. Performances of ELM-DEFS are compared with genetic algorithm (GA) and DEFS by means of performance evaluation metrics. Experimental results show that ELM-DEFS convergence is better than both DEFS and GA in selecting top-most feature subset; ELM-DEFS-based PNN (ELM-DEFS-PNN) classifier exhibits higher performance with an accuracy of 93% which is approximately 1% higher than GA-based PNN (GA-PNN) and DEFSbased PNN (DEFS-PNN) classification.