Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/51323

TítuloMulti-interval discretization of continuous attributes for label ranking
Autor(es)Sá, Cláudio Rebelo de
Soares, Carlos
Knobbe, Arno
Azevedo, Paulo J.
Jorge, Alípio Mário
Data2013
EditoraSpringer Verlag
RevistaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Resumo(s)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. © 2013 Springer-Verlag.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/51323
ISBN9783642408960
DOI10.1007/978-3-642-40897-7_11
ISSN0302-9743
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:HASLab - Artigos em atas de conferências internacionais (texto completo)

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