Journals / Turkish Journal of Electrical Engineering and Computer Science / 2004 / Cilt: 12 - Sayı: 1

Mining Classification Rules by Using Genetic Algorithms with Non-random Initial Population and Uniform Operator

Pages
43–52
Publication Date
2004-01-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data:Initial population is generated by methodically eliminating the randomness by generalized uniform population method.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Data Mining · Classification Rules · Genetic Algorithms

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.

Özet

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.

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Cite This Article

GÜNDOĞAN, K. K., ALATAŞ, B., KARCI, A. (2004). Mining Classification Rules by Using Genetic Algorithms with Non-random Initial Population and Uniform Operator. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 12 - Sayı: 1, 43–52. https://ajindex.com/articles/189069

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