Clustering-Based Feature Selection in Semi-supervised Problems

— In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination of supervised and unsupervised feature distance measure, which is based on Conditional Mutual Information and Conditional Entropy. Real databases were analyzed with different ratios between labelled and unlabelled samples in the training set, showing the satisfactory behaviour of the proposed approach.

• Type: Conference
• Year: 2009
• Where: ISDA
• Authors: Ianisse Quinzán, José Manuel Sotoca, Filiberto Pla
• Keywords: Feature Selection Method, ISDA 2009, Method Selects Variables, Operating Systems, Unsupervised Feature Distance