MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank
Joint Authors
Zhang, Xingyi
Cheng, Fan
Guo, Wei
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Learning to rank has attracted increasing interest in the past decade, due to its wide applications in the areas like document retrieval and collaborative filtering.
Feature selection for learning to rank is to select a small number of features from the original large set of features which can ensure a high ranking accuracy, since in many real ranking applications many features are redundant or even irrelevant.
To this end, in this paper, a multiobjective evolutionary algorithm, termed MOFSRank, is proposed for feature selection in learning to rank which consists of three components.
First, an instance selection strategy is suggested to choose the informative instances from the ranking training set, by which the redundant data is removed and the training efficiency is enhanced.
Then on the selected instance subsets, a multiobjective feature selection algorithm with an adaptive mutation is developed, where good feature subsets are obtained by selecting the features with high ranking accuracy and low redundancy.
Finally, an ensemble strategy is also designed in MOFSRank, which utilizes these obtained feature subsets to produce a set of better features.
Experimental results on benchmark data sets confirm the advantage of the proposed method in comparison with the state-of-the-arts.
American Psychological Association (APA)
Cheng, Fan& Guo, Wei& Zhang, Xingyi. 2018. MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank. Complexity،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1135938
Modern Language Association (MLA)
Cheng, Fan…[et al.]. MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank. Complexity No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1135938
American Medical Association (AMA)
Cheng, Fan& Guo, Wei& Zhang, Xingyi. MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank. Complexity. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1135938
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135938