Decision Tree and Instance-Based Learning for Label Ranking
Abstract
The label ranking problem consists of learning a model that maps instances to total orders over a finite set of predefined labels. This paper introduces new methods for label ranking that complement and improve upon existing approaches. More specifically, we propose extensions of two methods that have been used extensively for classification and regression so far, namely instance-based learning and decision tree induction. The unifying element of the two methods is a procedure for locally estimating predictive probability models for label rankings.
Cite
Text
Cheng et al. "Decision Tree and Instance-Based Learning for Label Ranking." International Conference on Machine Learning, 2009. doi:10.1145/1553374.1553395Markdown
[Cheng et al. "Decision Tree and Instance-Based Learning for Label Ranking." International Conference on Machine Learning, 2009.](https://mlanthology.org/icml/2009/cheng2009icml-decision/) doi:10.1145/1553374.1553395BibTeX
@inproceedings{cheng2009icml-decision,
title = {{Decision Tree and Instance-Based Learning for Label Ranking}},
author = {Cheng, Weiwei and Huhn, Jens C. and Hüllermeier, Eyke},
booktitle = {International Conference on Machine Learning},
year = {2009},
pages = {161-168},
doi = {10.1145/1553374.1553395},
url = {https://mlanthology.org/icml/2009/cheng2009icml-decision/}
}