Entropy-based discretization methods for ranking data

• Type: Article in International Scientific Journal
• Date: 2016

Authors

de Sa, CR (Author) Other; Carlos Soares (Author) FEUP; Knobbe, A (Author) Other

Journal

Title: Information Sciences

Vol. 329

Pages: 921-936

ISSN: 0020-0255

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 42 Citations

Scopus - 48 Citations

Other information

Authenticus ID: P-00J-ZHE

DOI: 10.1016/j.ins.2015.04.022

Abstract (EN): Label Ranking (LR) problems are becoming increasingly important in Machine Learning. While there has been a significant amount of work on the development of learning algorithms for LR in recent years, there are not many pre-processing methods for LR Some methods, like Naive Bayes for LR and APRIORI-LR, cannot handle real-valued data directly. Conventional discretization methods used in classification are not suitable for LR problems, due to the different target variable. In this work, we make an extensive analysis of the existing methods using simple approaches. We also propose a new method called EDiRa (Entropy-based Discretization for Ranking) for the discretization of ranking data. We illustrate the advantages of the method using synthetic data and also on several benchmark datasets. The results clearly indicate that the discretization is performing as expected and also improves the results and efficiency of the learning algorithms.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 16