Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
International Journal of Soft Computing
Year: 2007  |  Volume: 2  |  Issue: 4  |  Page No.: 555 - 561

Comparison Between Traditional Data Mining Techniques and Entropy-based Adaptive Genetic Algorithm for Learning Classification Rules

V.N. Rajavarman and S.P. Rajagopalan    

Abstract: Genetic algorithm is one of the commonly used approaches on data mining. In this study, we apply a genetic algorithm approach for classification problems. Binary coding is adopted in which an individual in a population consists of a fixed number of rules that stand for a solution candidate. The evaluation function considers 4 important factors which are error rate, entropy measure, rule consistency and hole ratio, respectively. Adaptive asymmetric mutation is applied by the self-adaptation of mutation inversion probability from 1-0 (0-1). The generated rules are not disjoint but can overlap. The final conclusion for prediction is based on the voting of rules and the classifier gives all rules equal weight for their votes. Based on three databases, we compared our approach with several other traditional data mining techniques including decision trees, neural networks and naive bayes learning. The results show that our approach outperformed others on both the prediction accuracy and the standard deviation.

Fulltext    |   Related Articles   |   Back
   
 
 
 
  Related Articles

 
 
 
 
 
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility