Gender classification on skeletal remains: efficiency of metaheuristic algorithm method and optimized back propagation neural network

Abstract

In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists.