Turkish Journal of Electrical Engineering and Computer Sciences
DOI
-
Abstract
Classification is a supervised learning method that induces a classification model from a database and is one of the most commonly applied data mining task. The frequently employed techniques are decision tree or neural network-based classification algorithms. This work presents an efficient genetic algorithm (GA) for classification rule mining technique that discovers comprehensible IF-THEN rules using a generalized uniform population method and a uniform operator inspired from the uniform population method. Initial population is generated by methodically eliminating the randomness by generalized uniform population method. In the subsequence generations, genetic diversity is ensured and premature convergence is prevented by the uniform operator. From the experimental results, it was observed that, this method handled the problems of GAs in the task of classification and guaranteed to get rid of any local solution and rapidly found comprehensible rules.
Keywords
Data Mining, Classification Rules, Genetic Algorithms, Genetic Algorithm Performance
First Page
43
Last Page
52
Recommended Citation
GÜNDOĞAN, KORKUT KORAY; ALATAŞ, BİLAL; and KARCI, ALİ (2004) "Mining Classification Rules by Using Genetic Algorithms with Non-random Initial Population and Uniform Operator," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 12: No. 1, Article 4. Available at: https://journals.tubitak.gov.tr/elektrik/vol12/iss1/4
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
