A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis
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Medientyp:
E-Artikel
Titel:
A graph-based feature selection method for learning to rank using spectral clustering for redundancy minimization and biased PageRank for relevance analysis
Beteiligte:
Yeh, Jen-Yuan;
Tsai, Cheng-Jung
Erschienen:
National Library of Serbia, 2022
Erschienen in:
Computer Science and Information Systems, 19 (2022) 1, Seite 141-164
Sprache:
Englisch
DOI:
10.2298/csis201220042y
ISSN:
1820-0214;
2406-1018
Entstehung:
Anmerkungen:
Beschreibung:
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.