K., S. M. Anbananthen and Sainarayanan, Gopala and Chekima, Ali and Teo, Jason Tze Wi (2007) Artificial neural network tree approach in data mining. Malaysian Journal of Computer Science, 20 (1). pp. 51-62. ISSN 0127-9084
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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: | Article |
|---|---|
| Keyword: | Artificial neural network, Comprehensibility, Data mining, Decision tree |
| Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science |
| Department: | SCHOOL > School of Engineering and Information Technology |
| Depositing User: | ADMIN ADMIN |
| Date Deposited: | 19 May 2011 16:55 |
| Last Modified: | 16 Oct 2017 12:25 |
| URI: | https://eprints.ums.edu.my/id/eprint/2875 |
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