A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis

Computer Science and Information Systems

article

Jen-Yuan Yeh¹  Cheng-Jung Tsai² 

[1] Dept. of Operation, Visitor Service, Collection and Information Management, National Museum of Natural Science;Graduate Institute of Statistics and Information Science, National Changhua University of Education

关键词: Feature selection;    Feature similarity graph;    Spectral clustering;    Biased PageRank;    Learning to rank;    Information retrieval;   

DOI  :  10.2298/CSIS201220042Y

学科分类：土木及结构工程学

来源: Computer Science and Information Systems

【 摘 要 】

This paper addresses the feature selection problem in learning to rank (LTR). We propose a graph-based feature selection method, named FS-SCPR, which comprises four steps: (i) use ranking information to assess the similarity between features and construct an undirected feature similarity graph; (ii) apply spectral clustering to cluster features using eigenvectors of matrices extracted from the graph; (iii) utilize biased PageRank to assign a relevance score with respect to the ranking problem to each feature by incorporating each feature’s ranking performance as preference to bias the PageRank computation; and (iv) apply optimization to select the feature from each cluster with both the highest relevance score and most information of the features in the cluster. We also develop a new LTR for information retrieval (IR) approach that first exploits FS-SCPR as a preprocessor to determine discriminative and useful features and then employs Ranking SVM to derive a ranking model with the selected features. An evaluation, conducted using the LETOR benchmark datasets, demonstrated the competitive performance of our approach compared to representative feature selection methods and state-of-the-art LTR methods.

【 授权许可】

CC BY-NC-ND