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    政大機構典藏 > 理學院 > 資訊科學系 > 期刊論文 >  Item 140.119/64483
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/64483

    Title: Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression
    Authors: Ruan, Yu-Xun;Lin, Hsuan-Tien;Tsai, Ming-Feng
    Contributors: 資科系
    Keywords: List-wise ranking;Cost-sensitive;Regression;Reduction
    Date: 2014.02
    Issue Date: 2014-03-06 16:29:52 (UTC+8)
    Abstract: This paper proposes a novel ranking approach, cost-sensitive ordinal classification via regression (COCR), which respects the discrete nature of ordinal ranks in real-world data sets. In particular, COCR applies a theoretically sound method for reducing an ordinal classification to binary and solves the binary classification sub-tasks with point-wise regression. Furthermore, COCR allows us to specify mis-ranking costs to further improve the ranking performance; this ability is exploited by deriving a corresponding cost for a popular ranking criterion, expected reciprocal rank (ERR). The resulting ERR-tuned COCR boosts the benefits of the efficiency of using point-wise regression and the accuracy of top-rank prediction from the ERR criterion. Evaluations on four large-scale benchmark data sets, i.e., "Yahoo! Learning to Rank Challenge" and "Microsoft Learning to Rank,” verify the significant superiority of COCR over commonly used regression approaches.
    Relation: Information Retrieval, 17(1), 1-20
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1007/s10791-013-9219-2
    DOI: 10.1007/s10791-013-9219-2
    Appears in Collections:[資訊科學系] 期刊論文

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