A threshold fuzzy entropy based feature selection for medical database classification

Feature selection is one of the most common and critical tasks in database classification. It reduces the computational cost by removing insignificant features. Consequently, this makes the diagnosis process accurate and comprehensible. This paper presents the measurement of feature relevance based on fuzzy entropy, tested with a Radial Basis Function Network classifier for a medical database classification. Three feature selection strategies are devised to obtain the valuable subset of relevant features. Five benchmarked datasets, which are available in the UCI Machine Learning Repository, have been used in this work. The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets.