Comput. Biol. Med. - A threshold fuzzy entropy based feature selection for medical database classification.

Tópicos

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Resumo

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.

Resumo Limpo

featur select one common critic task databas classif reduc comput cost remov insignific featur consequ make diagnosi process accur comprehens paper present measur featur relev base fuzzi entropi test radial basi function network classifi medic databas classif three featur select strategi devis obtain valuabl subset relev featur five benchmark dataset avail uci machin learn repositori use work classif accuraci show propos method capabl produc good result fewer featur origin dataset

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