Similarity-based classification with dominance-based decision rules

Marcin Szeląg, Salvatore Greco, Roman Słowiński

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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We consider a similarity-based classification problem where a new case (object) is classified based on its similarity to some previously classified cases. In this process of case-based reasoning (CBR), we adopt the Dominance-based Rough Set Approach (DRSA), that is able to handle monotonic relationship “the more similar is object y to object x with respect to the considered features, the closer is y to x in terms of the membership to a given decision class X”. At the level of marginal similarity concerning single features, we consider this similarity in ordinal terms only. The marginal similarities are aggregated within induced decision rules describing monotonic relationship between comprehensive similarity of objects and their similarities with respect to single features.
Original languageEnglish
Title of host publicationRough Sets
Subtitle of host publicationInternational Joint Conference, IJCRS 2016, Santiago de Chile, Chile, October 7–11, 2016, Proceedings
EditorsVíctor Flores, Fernando Gomide, Andrzej Janusz, Claudio Meneses, Duoqian Miao, Georg Peters, Dominik Ślęzak, Guoyin Wang, Richard Weber, Yiyu Yao
Number of pages10
ISBN (Electronic)978-3319471600
ISBN (Print)978-3319471594
Publication statusPublished - Sept 2016
EventInternational Joint Conference on Rough Sets, IJCRS 2016 - Universidad de Chile, Santiago de Chile, Chile
Duration: 7 Oct 201611 Oct 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Joint Conference on Rough Sets, IJCRS 2016
CitySantiago de Chile


  • classification
  • similarity
  • case-based reasoning
  • dominance-based Rough Set Approach
  • decision rules


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