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
What has Mill to say about data mining?
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
A dynamic interaction between machine learning and the philosophy of science
Minds and Machines - Machine learning as experimental philosophy of science
Rough sets and gradual decision rules
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A new proposal for fuzzy rough approximations and gradual decision rule representation
Transactions on Rough Sets II
International Journal of Hybrid Intelligent Systems - Hybrid Intelligence using rough sets
Hi-index | 0.00 |
We propose a new fuzzy rough set approach which, differently from most 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 is based on the ordinal properties of fuzzy membership degrees only. 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 proposed approach to rule induction is also interesting from the viewpoint of philosophy supporting data mining and knowledge discovery, because it is concordant with the method of concomitant variations by John Stuart Mill. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility degrees of multiple premises, on one hand, and conclusion, on the other hand.