Wyniki wyszukiwania - BazTech

1

An evolutionary algorithm determining a defuzzyfication functional

Kosiński W., Markowska-Kaczmar U.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2007

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Vol. 11, No 1-2

47-58

EN

Order fuzzy numbers are defined that make it possible to deal with fuzzy inputs quantitatively, exactly in the same way as with real numbers, together with four algebraic operations. An approximation formula is given for a defuzzyfication functional that plays the main role when dealing with fuzzy controllers and fuzzy inference systems. A dedicated evolutionary algorithm is presented in order to determine the form of a functional when a training set is given. The form of a genotype composed of three types of chromosomes and the fitness function are given and Genetic operators are proposed.

2

Rule extraction from approximating neural network - a real challenge for evolutionary method

Markowska-Kaczmar U., Mularczyk K.

Systems Science

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2006

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Vol. 32, no 4

35-43

EN

The paper describes a modification of the MulGex method in order to use it to extract rules from approximating neural network. Originally the method was designed to extract prepositional rules from classification neural network. The rules are searched by evolutionary algorithms working on two levels. The rules are optimized using the Pareto approach. The main principle referring to premise part of a rule has been unchanged but the form of conclusion instead of the class label describes a formula which can be a linear function and is encoded as a list of coefficients or it takes a form of a tree whose inner nodes contain functions and operators, and leaves - identifiers of attributes and numeric constants. Although the results obtained in the experiments for three different data sets can be assumed as satisfactory, some changes improving MulGex efficiency are proposed at the end.

3

Evolutionary approach to rule extraction from medical data

Kwaśnicka H., Markowska-Kaczmar U., Osojca T.

Journal of Medical Informatics & Technologies

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2004

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Vol. 7

KB3--12

EN

In the paper the method called CGA based on a cooperating genetic algorithm is presented. The CGA is developed for searching a set of rules describing classes in classification problems on the basis of training examples. The details of the method, such as a schema of coding (a chromosome), and a fitness function are shortly described. The method is independent of the type of attributes and it allows choosing different evaluation functions. Developed method was tested using different benchmark data sets. Next, in order to evaluate the efficiency of CGA, it was tested using the Breast Cancer data set with 10 fold cross validation technique.