title: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.
abstract: Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.
authors.affiliation: Lawrence Berkeley National Laboratory, University of California at Berkeley, 1 Cyclotron Road, MS. 84-171, Berkeley, CA 94720, USA. hpeng@lbl.gov
authors.forename: Hanchuan
authors.initials: H
authors.lastname: Peng
authors.forename: Fuhui
authors.initials: F
authors.lastname: Long
authors.forename: Chris
authors.initials: C
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*