Statistical model for rough set approach to multicriteria classification

K. Dembczynski, Salvatore Greco, W. Kotlowski, R. Slowinski

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, Dominance-based Rough Set Approach (DRSA) has been introduced to deal with the problem of multicriteria classification. However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive, which led to the proposal of the Variable Consistency variant of DRSA. In this paper, we introduce a new approach to variable consistency that is based on maximum likelihood estimation. For two-class (binary) problems, it leads to the isotonic regression problem. The approach is easily generalized for the multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific risk-minimizing decision rule in statistical decision theory.
Original languageEnglish
Title of host publicationKnowledge discovery in databases: proceedings of the 11th european conference
EditorsJ. Kok, J. Koronacki, R. Lopez de Mantaras, S. Matwin, D. Mladenic, A. Skowron
Place of PublicationBerlin
PublisherSpringer
Pages164-175
Number of pages12
Volume4702
Edition4702
ISBN (Print)9783540749752
DOIs
Publication statusPublished - 2007

Publication series

NameLecture notes in computer science
PublisherSpringer
Number4702
ISSN (Print)0302-9743

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