On Minimizing the Position Error in Label Ranking
| Type of publication: | Techreport |
| Citation: | jf:TUD-KE-2007-04 |
| Number: | TUD-KE-2007-04 |
| Year: | 2007 |
| Institution: | TU Darmstadt, Knowledge Engineering Group |
| URL: | /publications/reports/tud-ke-2007-04.pdf |
| Abstract: | Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem is not to give a single estimation, but to make repeated suggestions until the correct target label has been identified. Thus, the learner has to deliver a label ranking, that is, a ranking of all possible alternatives. In this paper, we discuss a loss function, called the position error, which is suitable for evaluating the performance of a label ranking algorithm in this setting. Moreover, we propose “ranking through iterated choice”, a general strategy for extending any multi-class classifier to this scenario. Its basic idea is to reduce label ranking to standard classification by successively predicting a most likely class label and retraining a model on the remaining classes. We demonstrate empirically that this procedure does indeed reduce the position error in comparison with a conventional approach that ranks the classes according to their estimated probabilities. Besides, we also address the issue of implementing ranking through iterated choice in a computationally efficient way. |
| Keywords: | |
| Authors | |
|
Attachments
|
|
|
|
|
Topics
|
|
|
|
