An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification

Abstract

The classification of humanoid locomotion is a troublesome exercise because of non-linearity associate with gait signals. The classification using the different machine learning technique leads for over fitting and under fitting. To select the optimized feature is a difficult task. The high dimension feature vector requires a high computational cost. The hand craft feature selection machine learning techniques performed poor. We have used the incremental feature selection strategy for feature selection. In this paper we first selected the feature and identify the principle feature then we classify gait data using different machine learning technique (KNN, ANN, SVM, DNN and classifier fusion) and shown the performance comparison. Experimental result on real time datasets propose method is better than previous method as far as humanoid locomotion classification is concerned and the generalization accuracy provided by new feature selection method i.e. incremental feature selection (IFS) with analysis of variance (ANOVA) (Zhang et al., 20). During the feature extraction, 17 features were selected from the existing literatures (Wang et al. in IEEE Trans Circ Syst Video Technol 14(2):149–158, 15). Using all the features could lead to over fitting, information redundancy and dimension disaster. Thus, a system with optimal features was selected using ANOVA combined with IFA. These selected features were then fed as an input to the ANN, SVM, KNN and DNN model. These individual classifiers were combined to produce classifier fusion model. The 5-fold cross-validation was used to evaluate the performance of the proposed model. Based on the empirical results it may be concluded that classifier fusion provides satisfactory results (92.23 %) compared to other individual classifiers. One-way analysis of variance test, Friedman’s test and Kruskal-Wallis test has also been conducted to validate the statistical significance of the results. The proposed system can be used as recommender system based on behavioral gait pattern about the performance of player of Indian cricket team, Biometric and help to diagnosis Parkinson disease.