A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
Rough Set Approach to Decisions under Risk
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Dominance-Based Rough Set Approach Using Possibility and Necessity Measures
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Dominance-based rough set approach as a proper way of handling graduality in rough set theory
Transactions on rough sets VII
Generalizing rough set theory through dominance-based rough set approach
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
A new proposal for fuzzy rough approximations and gradual decision rule representation
Transactions on Rough Sets II
Dominance-Based rough set approach to case-based reasoning
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
A fuzzy view on rough satisfiability
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
DRSA decision algorithm analysis in stylometric processing of literary texts
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
On performance of DRSA-ANN classifier
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Reduct-based analysis of decision algorithms: application in computational stylistics
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Application of DRSA-ANN classifier in computational stylistics
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Satisfiability judgement under incomplete information
Transactions on Rough Sets XI
Rough set-based analysis of characteristic features for ANN classifier
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Rule-Based approach to computational stylistics
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Satisfiability of Formulas from the Standpoint of Object Classification: The RST Approach
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Decision rule length as a basis for evaluation of attribute relevance
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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Dominance-based Rough Set Approach (DRSA) has been proposed by the authors to handle background knowledge about ordinal evaluations of objects from a universe, and about monotonic relationships between these evaluations, e.g. "the larger the mass and the smaller the distance, the larger the gravity" or "the greater the debt of a firm, the greater its risk of failure". Such a knowledge is typical for data describing various phenomena, and for data concerning multiple criteria decision making or decision under uncertainty. It appears that the Indiscernibility-based Rough Set Approach (IRSA) proposed by Pawlak involves a primitive idea of monotonicity related to a scale with only two values: "presence" and "absence" of a property. This is why IRSA can be considered as a particular case of DRSA. Monotonicity gains importance when the binary scale, including only "presence" and "absence" of a property, becomes finer and permits to express the presence of a property to certain degree. This observation leads to very natural fuzzy generalization of the rough set concept via DRSA. It exploits only ordinal properties of membership degrees and monotonic relationships between them, without using any fuzzy connective. We show, moreover, that this generalization is a natural continuation of the ideas given by Leibniz, Frege, Boole, 茂戮驴ukasiewicz and Pawlak. Finally, the fuzzy rough approximations taking into account monotonic relationships between memberships to different sets can be applied to case-based reasoning. In this perspective, we propose to consider monotonicity of the type: "the more similar is yto x, the more credible is that ybelongs to the same set as x".