Name
Title | ||
|---|---|---|
Improving ranking performance with cost-sensitive ordinal classification via regression |
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10791-013-9219-2 | Inf. Retr. |
Keywords | Field | DocType |
List-wise ranking,Cost-sensitive,Regression,Reduction | Data mining,Learning to rank,Data set,Binary classification,Regression,Ranking,Computer science,Ordinal number,Ordinal data,Ordinal regression,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
17 | 1 | 1386-4564 |
Citations | PageRank | References |
3 | 0.42 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Xun Ruan | 1 | 3 | 0.42 |
| Hsuan-Tien Lin | 2 | 829 | 74.77 |
| Ming-Feng Tsai | 3 | 1024 | 45.13 |