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Artificial Neural Network Tree Approach In Data Mining

Muthu Anbananthen, Kalaiarasi Sonai and Sainarayanan, Gopalakrishnan and Chekima, Ali and Teo, Jason (2007) Artificial Neural Network Tree Approach In Data Mining. Malaysian Journal of Computer Science, 20 (1). pp. 51-62. ISSN 0127-9084

Full text not available from this repository.

Official URL: http://mjcs.fsktm.um.edu.my/detail.asp?AID=494

Affiliations

Universiti Malaysia Sabah
New Horizon College of Engineering

Abstract

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of real world problems. However, there are strong arguments as to why ANNs are insufficient for data mining. The arguments are the poor comprehensibility of the learned ANNs, which is the inability to represent the learned knowledge in an understandable way to the users. In this paper, Artificial Neural Network Tree (ANNT), i.e. ANN training preceded by Decision Tree rules extraction method, is presented to overcome the comprehensibility problem of ANN. Experimental results on three data sets show that the proposed algorithm generates rules that are better than C4.5. This paper provides an evaluation of the proposed method in terms of accuracy, comprehensibility and fidelity.

Item Type:Journal
Keywords:Data mining, Comprehensibility, Artificial Neural Network, Decision Tree
Subjects:Q Science
ID Code:255

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