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doi:10.1016/S0957-4174(03)00005-8    
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Copyright © 2003 Elsevier Science Ltd. All rights reserved.

The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance

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P. L. HsuE-mail The Corresponding Author, a, R. LaiE-mail The Corresponding Author, a and C. C. ChiuCorresponding Author Contact Information, E-mail The Corresponding Author, b

a Department of Computer Science and Engineering, Yuan Ze University, Taiwan, ROC

b Department of Information Management, Yuan Ze University, Taiwan, ROC


Available online 11 March 2003.


Referred to by:Erratum to “The hybrid of association rule algorithms and genetic algorithms for tree induction: an example of predicting the student course performance” [Expert Systems with Applications 25 (2003) 51–62]
Expert Systems with Applications, Volume 25, Issue 3, October 2003, Page 467,
P. L. Hsu, R. Lai, C. C. Chiu, C. I. Hsu
PDF (30 K)

Abstract

Revealing valuable knowledge hidden in corporate data becomes more critical for enterprise decision making. When more data is collected and accumulated, extensive data analysis would not be easier without effective and efficient data mining methods. This paper proposes a hybrid of the association rule algorithm and genetic algorithms (GAs) approach to discover a classification tree. The association rule algorithm is adopted to obtain useful clues based on which the GA is able to proceed its searching tasks in a more efficient way. In addition an association rule algorithm is employed to acquire the insights for those input variables most associated with the outcome variable before executing the evolutionary process. These derived insights are converted into GA's seeding chromosomes. The proposed approach is experimented and compared with a regular genetic algorithm in predicting a student's course performance.

Author Keywords: Genetic algorithms; Association rule; Classification trees; Student course performance

Article Outline

1. Introduction
2. The literature review
2.1. Genetic algorithm for rule induction
2.2. Classification trees
2.3. Association rules algorithms for attributes selection
3. The hybrid of association rule algorithms and genetic algorithms (AGA)
4. The experiments and results
4.1. The application of house-votes data set
4.2. The application of student learning performance data set
4.2.1. The data description
5. Discussion
6. Conclusions and future development
Acknowledgements
References











Corresponding Author Contact InformationCorresponding author. Fax: +886-3-4352077


 
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