@conference {DBLP:conf/dis/SaSKAJ13,
	title = {Multi-interval Discretization of Continuous Attributes for Label Ranking},
	booktitle = {16th International Conference on Discovery Science - DS},
	year = {2013},
	month = {October},
	pages = {155-169},
	address = {Singapore},
	abstract = {<p>Label Ranking (LR) problems, such as predicting rankings of nancial analysts, are becoming increasingly important in data mining. While there has been a signicant amount of work on the development of learning algorithms for LR in recent years, preprocessing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consider conventional discretization methods used in classication, by simply treating each unique ranking as a separate class. In this paper, we show that such an approach has several disadvantages. As an alternative, we propose an adaptation of an existing method, MDLP, specically for LR problems. We illustrate the advantages of the new method using synthetic data. Additionally, we present results obtained on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and in most cases improves the results of the learning algorithms.</p>
},
	attachments = {https://haslab.uminho.pt/sites/default/files/pja/files/ds2013.pdf},
	author = {Cl{\'a}udio Rebelo S{\'a} and Carlos Soares and Arno Knobbe and Paulo Jorge Azevedo and Al{\'\i}pio M{\'a}rio Jorge}
}