Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable

:  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; text categorization; visualization; machine learning classification; Feature filtering methods are often used in text classification and other high-dimensional domains to quickly score each feature independently and pass only the best to the learning algorithm. The panoply of available methods grows over the years, with frequent research publications touting new functions that seem to yield superior learning: variants on Information Gain, Chi Squared, Mutual Information, and others. This slow generate-and-test search in the literature is usually counted as progress towards finding superior filter methods. But this is illusory. We provide a new empirical method to reveal the feature preference surface for a given dataset and classifier: cross-validating with an additional feature whose noise characteristics ar...

• Type: Journal
• Year: 2012
• Where: SDM
• Authors: George Forman
• Keywords: Data Mining, Optimal Feature Selection, SDM 2012, Sensitivity And Specificity, Target Concept