Expected Divergence Based Feature Selection for Learning to Rank
نویسندگان
چکیده
(i) RankSVM SVM based pairwise ranker. (ii) RankBoost Weak ranker based pairwise ranker that uses boosting. (iii) LambdaMART LambdaMART uses gradient boosting to optimize a ranking cost function. Baseline 1: FS-BFS The FS-BFS is a wrapper based approach of feature selection for ranking [Dang and Croft, 2010]. The method partitions the F into non-overlapping k subsets and learns a ranking model which maximizes the performance over that subset of features. Best first search is used on the undirected graph of features to extract subsets and the weights of the features are learnt using coordinate ascent.
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تاریخ انتشار 2012