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https://ah.lib.nccu.edu.tw/handle/140.119/64483
題名: | Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression | 作者: | Ruan, Yu-Xun ; Lin, Hsuan-Tien ; Tsai, Ming-Feng 蔡銘峰 |
貢獻者: | 資科系 | 關鍵詞: | List-wise ranking ; Cost-sensitive ; Regression ; Reduction | 日期: | 2014 | 上傳時間: | 6-Mar-2014 | 摘要: | 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. | 關聯: | Information Retrieval, 17(1), 1-20 | 資料類型: | article | DOI: | http://dx.doi.org/10.1007/s10791-013-9219-2 |
Appears in Collections: | 期刊論文 |
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