A Feature Selection Application Using Particle Swarm Optimization for Learning Concept Detection

Recent developments of computational intelligence on educational technology yield concept map mining as a new research area. Concept map mining covers the extraction of learning concepts, specifying relations among them, and generating a concept map from educational contents. In this study, we focused on determining the features that characterize a learning concept extracted from an educational text as raw data. The first three features are detected by using a hybrid system of Multi Layer Perceptron (MLP) and Particle Swarm Optimization (PSO), and the performance of the applied method is gauged in the viewpoint of a typical classification problem.