MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank
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 ensur...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7837696 |
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| author | Fan Cheng Wei Guo Xingyi Zhang |
| author_facet | Fan Cheng Wei Guo Xingyi Zhang |
| author_sort | Fan Cheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8718ef0c5b86417c8b4fa790baf53dd5 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8718ef0c5b86417c8b4fa790baf53dd52025-02-03T01:24:04ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/78376967837696MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to RankFan Cheng0Wei Guo1Xingyi Zhang2Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei 230039, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaKey Laboratory of Intelligent Computing & Signal Processing of Ministry of Education, Anhui University, Hefei 230039, ChinaLearning 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.http://dx.doi.org/10.1155/2018/7837696 |
| spellingShingle | Fan Cheng Wei Guo Xingyi Zhang MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank Complexity |
| title | MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank |
| title_full | MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank |
| title_fullStr | MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank |
| title_full_unstemmed | MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank |
| title_short | MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank |
| title_sort | mofsrank a multiobjective evolutionary algorithm for feature selection in learning to rank |
| url | http://dx.doi.org/10.1155/2018/7837696 |
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