Feature selection in a fuzzy student sectioning algorithm

Abstract

In this paper a new student sectioning algorithm is proposed. In this method a fuzzy clustering, a fuzzy evaluator and a novel feature selection method is used. Each student has a feature vector, contains his taken courses as its feature elements. The best features are selected for sectioning based on removing those courses that the most or the fewest numbers of students have taken. The Fuzzy c-Means classifier classifies students. After that, a fuzzy function evaluates the produced clusters based on two criteria: balancing sections and students' schedules similarity within each section. These are used as linguistic variables in a fuzzy inference engine. The selected features determine the best students' sections. Simulation results show that improvement in sectioning performance is about 18% in comparison with considering all of the features, which not only reduces the feature vector elements but also increases the computing performance. © Springer-Verlag Berlin Heidelberg 2005.