Unsupervised bidirectional feature selection based on contribution entropy for medical databases
by D. Devakumari, K. Thangavel, K. Sarojini
International Journal of Healthcare Technology and Management (IJHTM), Vol. 12, No. 5/6, 2011

Abstract: Feature selection is one of the important pre-processing steps in data mining for selecting informative feature subsets in large noisy data sets. This paper proposes an unsupervised feature selection method known as bidirectional selection based on the contribution entropy of individual features. The proposed feature selection method was tested on benchmark medical data sets, and the quality of the clusters obtained was evaluated using the homogeneity and separation ratio. Results show an improvement in cluster quality when compared with existing feature selection methods.

Online publication date: Sat, 28-Mar-2015

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