Case-based reasoning using dominance-based decision rules

Authors:
Marcin Szeląg;Salvatore Greco;Jerzy Błaszczyński;Roman Słowiński
Affiliations:
Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Faculty of Economics, University of Catania, Catania, Italy;Institute of Computing Science, Poznań University of Technology, Poznań, Poland;Institute of Computing Science, Poznań University of Technology, Poznań, Poland and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Venue:
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Year:
2011

Citing 7
Cited 0

Case-based reasoning

Case-based reasoning
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Variable Consistency Model of Dominance-Based Rough Sets Approach

RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Monotonic Variable Consistency Rough Set Approaches

International Journal of Approximate Reasoning
Case-based reasoning using gradual rules induced from dominance-based rough approximations

RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Sequential covering rule induction algorithm for variable consistency rough set approaches

Information Sciences: an International Journal
Dominance-Based rough set approach to case-based reasoning

MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Case-based Reasoning (CBR) is a process of inferring conclusions related to a new situation by the analysis of similar cases known from the past experience. We propose to adopt in this process the Dominance-based Rough Set Approach (DRSA), that is able to handle monotonicity relationships of the type "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 fuzzy set X". At the level of marginal similarity concerning single features, we consider this similarity in ordinal terms only. The marginal similarities are aggregated within decision rules underlying the general monotonicity property of comprehensive closeness of objects with respect to their marginal similarities.