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Dominance-based rough set approach to case-based reasoning

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

Case-based reasoning is a paradigm in machine learning whose idea is that a new problem can be solved by noticing its similarity to a set of problems previously solved. We propose a new approach to case-based reasoning. It is based on rough set theory that is a mathematical theory for reasoning about data. More precisely, we adopt Dominance-based Rough Set Approach (DRSA) that is particularly appropriate in this context for its ability of handling monotonicity relationship between ordinal properties of data related to monotonic relationships between attribute values in the considered data set. In general terms, monotonicity concerns relationship between different aspects of a phenomenon described by data: for example, "the larger the house, the higher its price" or "the closer the house to the city centre, the higher its price". In the perspective of case-based reasoning, we propose to consider monotonicity of the type "the more similar is y to x, the more credible is that y belongs to the same set as x". We show that rough approximations and decision rules induced from these approximations can be redefined in this context and that they satisfy the same fundamental properties of classical rough set theory.
Original languageEnglish
Title of host publicationModeling decisions for artificial intelligence: proceedings of the 3rd international conference
EditorsV. Torra, Y. Narukawa, A. Valls, J. Domingo-Ferrer
Place of PublicationBerlin
PublisherSpringer
Pages7-18
Number of pages12
Volume3885
Edition3885
ISBN (Print)9783540327806
DOIs
Publication statusPublished - 2006

Publication series

NameLecture notes in computer science
PublisherSpringer
Number3885
ISSN (Print)0302-9743

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