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MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank

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  • Fan Cheng
  • Wei Guo
  • Xingyi Zhang

Abstract

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.

Suggested Citation

  • Fan Cheng & Wei Guo & Xingyi Zhang, 2018. "MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank," Complexity, Hindawi, vol. 2018, pages 1-14, December.
  • Handle: RePEc:hin:complx:7837696
    DOI: 10.1155/2018/7837696
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    References listed on IDEAS

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    1. Mehrnoush Barani Shirzad & Mohammad Reza Keyvanpour, 2018. "A Systematic Study of Feature Selection Methods for Learning to Rank Algorithms," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 8(3), pages 46-67, July.
    2. Yuan Lin & Hongfei Lin & Kan Xu & Xiaoling Sun, 2013. "Learning to rank using smoothing methods for language modeling," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(4), pages 818-828, April.
    3. Yuan Lin & Hongfei Lin & Kan Xu & Xiaoling Sun, 2013. "Learning to rank using smoothing methods for language modeling," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(4), pages 818-828, April.
    4. Jaesung Lee & Wangduk Seo & Dae-Won Kim, 2018. "Effective Evolutionary Multilabel Feature Selection under a Budget Constraint," Complexity, Hindawi, vol. 2018, pages 1-14, March.
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