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Discovering Fuzzy Classification Rules with Genetic Programming and CoEvolution
 Principles of Data Mining and Knowledge Discovery, Lecture Notes in Artificial Intelligence
, 2001
"... In essence, data mining consists of extracting knowledge from data. This paper proposes a coevolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evo ..."
Abstract

Cited by 28 (1 self)
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In essence, data mining consists of extracting knowledge from data. This paper proposes a coevolutionary system for discovering fuzzy classification rules. The system uses two evolutionary algorithms: a genetic programming (GP) algorithm evolving a population of fuzzy rule sets and a simple evolutionary algorithm evolving a population of membership function definitions. The two populations coevolve, so that the final result of the coevolutionary process is a fuzzy rule set and a set of membership function definitions which are well adapted to each other. In addition, our system also has some innovative ideas with respect to the encoding of GP individuals representing rule sets. The basic idea is that our individual encoding scheme incorporates several syntactical restrictions that facilitate the handling of rule sets in disjunctive normal form. We have also adapted GP operators to better work with the proposed individual encoding scheme.
A Mathematical study of Discover The Exception Within The Rough set Framework Approach
"... The approach used by concept discovery and information reengineering is flexible and dynamic in that the conceptual integration process can be frequent activity. As usage patterns are utilized to discover concepts further. The information reengineering approach presented here addresses the uniquene ..."
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The approach used by concept discovery and information reengineering is flexible and dynamic in that the conceptual integration process can be frequent activity. As usage patterns are utilized to discover concepts further. The information reengineering approach presented here addresses the uniqueness of each user group and allows
Jacobus van Zyl, B.Sc. B.Eng. M.Sc.
"... In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a the ..."
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In 1965 Lofti A. Zadeh proposed fuzzy sets as a generalization of crisp (or classic) sets to address the incapability of crisp sets to model uncertainty and vagueness inherent in the real world. Initially, fuzzy sets did not receive a very warm welcome as many academics stood skeptical towards a theory of “imprecise ” mathematics. In the middle to late 1980’s the success of fuzzy controllers brought fuzzy sets into the limelight, and many applications using fuzzy sets started appearing. In the early 1970’s the first machine learning algorithms started appearing. The AQ (for Aq) family of algorithms pioneered by Ryszard S. Michalski is a good example of the family of set covering algorithms. This class of learning algorithm induces concept descriptions by a greedy construction of rules that describe (or cover) positive training examples but not negative training examples. The learning process is iterative, and in each iteration one rule is induced and the positive examples covered by the rule removed from the set of positive training examples. Because positive instances are separated from negative instances, the term separateandconquer has been used to contrast the learning strategy against decision tree induction that use a divideandconquer learning strategy. This dissertation proposes fuzzy set covering as a powerful rule induction strategy. We survey existing