Classifier-independent feature selection on the basis of divergence criterion

Feature selection aims to choose a feature subset that has the most discriminative information from the original feature set. In practical cases, it is preferable to select a feature subset that is universally effective for any kind of classifier because there is no underlying information about a given dataset. Such a trial is called classifier-independent feature selection. We took notice of Novovicova

• Type: Journal
• Year: 2006
• Where: PAA
• Authors: Naoto Abe, Mineichi Kudo, Jun Toyama, Masaru Shimbo
• Keywords: Classifier-independent Feature Selection, Distributed And Parallel Computing, Feature Selection, Novovicova´ Et Al, PAA 2006