Efficient feature weighting methods for ranking

Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generalization performance of ranking functions in information retrieval. Due to fundamental differences between classification and ranking, feature weighting methods developed for classification cannot be readily applied to feature weighting for ranking. A state of the art feature selection method for ranking, called GAS, has been recently proposed, which exploits importance of each feature and similarity between every pair of features. However, GAS must compute the similarity scores of all pairs of features, thus it is not scalable for high-dimensional data and its performance degrades on nonlinear ranking functions. This paper proposes novel algorithms, RankWrapper and RankFilter, which is scalable for high-dimensional data and also performs reasonably well on nonlinear ranking functions. RankWrapper and RankFilte...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Hwanjo Yu, Jinoh Oh, Wook-Shin Han
• Keywords: CIKM 2009, Feature Weighting, Information Management, Nonlinear Ranking Functions, Ranking Functions