A logic for fuzzy data analysis
Fuzzy Sets and Systems - Special issue on applications of fuzzy systems theory, Iizuka '88
Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
Measurement-theoretic justification of connectives in fuzzy set theory
Fuzzy Sets and Systems
Rough-set reasoning about uncertain data
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Fuzzy Extension of Rough Sets Theory
RSCTC '98 Proceedings of the First 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
Data mining tasks and methods: Classification: multicriteria classification
Handbook of data mining and knowledge discovery
Possibility and necessity measure specification using modifiers for decision making under fuzziness
Fuzzy Sets and Systems - Special issue: Preference modelling and applications
Dominance-Based Rough Set Approach to Reasoning About Ordinal Data
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Attribute Reduction Based on Fuzzy Rough Sets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Rough Set Approach to Knowledge Discovery about Preferences
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
Fuzzy rough sets and multiple-premise gradual decision rules
International Journal of Approximate Reasoning
Fuzzy rough sets and multiple-premise gradual decision rules
WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
Decision table reduction in KDD: fuzzy rough based approach
Transactions on Rough Sets XI
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We propose a new fuzzy rough set approach which, differently from most of known fuzzy set extensions of rough set theory, does not use any fuzzy logical connectives (t-norm, t-conorm, fuzzy implication). As there is no rationale for a particular choice of these connectives, avoiding this choice permits to reduce the part of arbitrary in the fuzzy rough approximation. Another advantage of the new approach is that it uses only the ordinal property of fuzzy membership degrees. The concepts of fuzzy lower and upper approximations are thus proposed, creating a base for induction of fuzzy decision rules having syntax and semantics of gradual rules. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility of premise and conclusion; for this reason, there are four types of decision rules. In addition to decision rule representation, a new scheme of inference with a generalized fuzzy rough modus ponens is proposed.