Investigation of Data Mining Using Pruned Artificial Neural Network Tree
A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. In this paper, Artificial Neural...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Malaya
2008
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| Online Access: | https://eprints.ums.edu.my/id/eprint/21905/1/Investigation%20of%20Data%20Mining%20Using%20Pruned%20Artificial%20Neural%20Network%20Tree.pdf |
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| author | Kalaiarasi, S. M. A. Sainarayanan, Gopala Ali Chekima Jason Teo |
| author_facet | Kalaiarasi, S. M. A. Sainarayanan, Gopala Ali Chekima Jason Teo |
| author_sort | Kalaiarasi, S. M. A. |
| collection | UMS |
| description | A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. 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. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity. |
| first_indexed | 2024-03-06T02:59:17Z |
| format | Article |
| id | ums.eprints-21905 |
| institution | Universiti Malaysia Sabah |
| language | English |
| last_indexed | 2024-03-06T02:59:17Z |
| publishDate | 2008 |
| publisher | University of Malaya |
| record_format | dspace |
| spelling | ums.eprints-219052019-04-30T07:17:38Z https://eprints.ums.edu.my/id/eprint/21905/ Investigation of Data Mining Using Pruned Artificial Neural Network Tree Kalaiarasi, S. M. A. Sainarayanan, Gopala Ali Chekima Jason Teo A major drawback associated with the use of artificial neural networks for data mining is their lack of explanation capability. While they can achieve a high predictive accuracy rate, the knowledge captured is not transparent and cannot be verified by domain experts. 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. Two pruning techniques are used with the ANNT algorithm; one is to prune the neural network and another to prune the tree. Both of these pruning methods are evaluated to see the effect on ANNT in terms of accuracy, comprehensibility and fidelity. University of Malaya 2008 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/21905/1/Investigation%20of%20Data%20Mining%20Using%20Pruned%20Artificial%20Neural%20Network%20Tree.pdf Kalaiarasi, S. M. A. and Sainarayanan, Gopala and Ali Chekima and Jason Teo (2008) Investigation of Data Mining Using Pruned Artificial Neural Network Tree. Malaysian Journal of Computer Science, 3 (3). pp. 188-201. ISSN 0127-9084 3(3) · December 2008 |
| spellingShingle | Kalaiarasi, S. M. A. Sainarayanan, Gopala Ali Chekima Jason Teo Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title | Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title_full | Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title_fullStr | Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title_full_unstemmed | Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title_short | Investigation of Data Mining Using Pruned Artificial Neural Network Tree |
| title_sort | investigation of data mining using pruned artificial neural network tree |
| url | https://eprints.ums.edu.my/id/eprint/21905/1/Investigation%20of%20Data%20Mining%20Using%20Pruned%20Artificial%20Neural%20Network%20Tree.pdf |
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