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

Korkut Koray GÜNDOĞAN; Bilal ALATAŞ; Ali KARCI

Makale detayları

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

Yazarlar:

Korkut Koray GÜNDOĞAN | Bilal ALATAŞ | Ali KARCI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2004 Vol.12 Issue 1, pp.43-52

Belge Tipi:

Makale

Yayın Tarihi:

12/2003

Belge Dili:

Türkçe

Document ID (Org No.):

2000744265

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

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.

 

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.

Makale detayları

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

Yazarlar: Korkut Koray GÜNDOĞAN | Bilal ALATAŞ | Ali KARCI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2004 Vol.12 Issue 1, pp.43-52

Belge Tipi: Makale

Yayın Tarihi: 12/2003

Belge Dili: Türkçe

Document ID (Org No.): 2000744265

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Korkut Koray GÜNDOĞAN | Bilal ALATAŞ | Ali KARCI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2004 Vol.12 Issue 1, pp.43-52

Belge Tipi:

Makale

Yayın Tarihi:

12/2003

Belge Dili:

Türkçe

Document ID (Org No.):

2000744265

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632