title: A novel feature selection method and its application.
abstract: In this paper, a novel feature selection method based on rough sets and mutual information is proposed. The dependency of each feature guides the selection, and mutual information is employed to reduce the features which do not favor addition of dependency significantly. So the dependency of the subset found by our method reaches maximum with small number of features. Since our method evaluates both definitive relevance and uncertain relevance by a combined selection criterion of dependency and class-based distance metric, the feature subset is more relevant than other rough sets based methods. As a result, the subset is near optimal solution. In order to verify the contribution, eight different classification applications are employed. Our method is also employed on a real Alzheimer's disease dataset, and finds a feature subset where classification accuracy arrives at 81.3%. Those present results verify the contribution of our method.
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chu Avenue, Kowloon, Hong Kong.
authors.forename: Bing
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chu Avenue, Kowloon, Hong Kong.
authors.forename: Tommy W S
authors.initials: TW
authors.lastname: Chow
authors.affiliation: Computational Biology Branch, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
authors.forename: Di
authors.initials: D
authors.lastname: Huang
keywords: Alzheimer's disease; class-based distance metric; feature selection; mutual information; rough sets