Computer Science and Information Systems | |
A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis | |
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
【 预 览 】
Files | Size | Format | View |
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RO202307150003273ZK.pdf | 976KB | download |