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| Title | Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression |
| Creator | Ruan, Yu-Xun ; Lin, Hsuan-Tien ; Tsai, Ming-Feng 蔡銘峰 |
| Contributor | 資科系 |
| Key Words | List-wise ranking ; Cost-sensitive ; Regression ; Reduction |
| Date | 2014.02 |
| Date Issued | 6-Mar-2014 16:29:52 (UTC+8) |
| Summary | 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 |
| Type | article |
| DOI | http://dx.doi.org/10.1007/s10791-013-9219-2 |
| dc.contributor | 資科系 | en_US |
| dc.creator (作者) | Ruan, Yu-Xun ; Lin, Hsuan-Tien ; Tsai, Ming-Feng | en_US |
| dc.creator (作者) | 蔡銘峰 | zh_TW |
| dc.date (日期) | 2014.02 | en_US |
| dc.date.accessioned | 6-Mar-2014 16:29:52 (UTC+8) | - |
| dc.date.available | 6-Mar-2014 16:29:52 (UTC+8) | - |
| dc.date.issued (上傳時間) | 6-Mar-2014 16:29:52 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/64483 | - |
| dc.description.abstract (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. | en_US |
| dc.format.extent | 488520 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Information Retrieval, 17(1), 1-20 | en_US |
| dc.subject (關鍵詞) | List-wise ranking ; Cost-sensitive ; Regression ; Reduction | en_US |
| dc.title (題名) | Improving Ranking Performance with Cost-sensitive Ordinal Classification via Regression | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/s10791-013-9219-2 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/s10791-013-9219-2 | en_US |