Multi-interval Discretization of Continuous Attributes for Label Ranking

• Type: Article in International Conference Proceedings Book
• Date: 2013

Authors

Claudio Rebelo de Sa (Author) Other; Carlos Soares (Author) FEUP; Arno Knobbe (Author) Other; Paulo Azevedo (Author) Other; Alipio Mario Jorge (Author) FCUP

Conference proceedings International

Title: DISCOVERY SCIENCE

Pages: 155-169

16th International Conference on Discovery Science (DS)

Singapore, SINGAPORE, OCT 06-09, 2013

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 12 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-008-HKZ

DOI: 10.1007/978-3-642-40897-7_11

Abstract (EN): Label Ranking (LR) problems, such as predicting rankings of financial analysts, are becoming increasingly important in data mining. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing 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 classification, 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, specifically 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 some cases improves the results of the learning algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15