Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT
Interest in the application of neural networks as tools for decision support has been growing in recent years. A major drawback often associated with neural networks is the difficulty in understanding the knowledge represented by a trained network. This paper describes an approach that can extract s...
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| Veröffentlicht in: | Information & management 1998-09, Vol.34 (2), p.91-101 |
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| container_title | Information & management |
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| creator | Setiono, Rudy Thong, James Y.L Yap, Chee-Sing |
| description | Interest in the application of neural networks as tools for decision support has been growing in recent years. A major drawback often associated with neural networks is the difficulty in understanding the knowledge represented by a trained network. This paper describes an approach that can extract symbolic rules from neural networks. We illustrate how the approach successfully extracted rules from a data set collected from a survey of the service sectors in the United Kingdom. The extracted rules were then used to distinguish between organizations using computers from those that do not. The classification scheme based on these rules was used to identify specific segments of a market for promoting adoption of information technology. The extracted rules are not only concise but also outperform discriminant analysis in terms of predictive accuracy. |
| doi_str_mv | 10.1016/S0378-7206(98)00048-2 |
| format | Article |
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Neural networks</subject><subject>Discriminant analysis</subject><subject>Exact sciences and technology</subject><subject>Information and communication sciences</subject><subject>Information and communication technologies</subject><subject>Information science. 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| ispartof | Information & management, 1998-09, Vol.34 (2), p.91-101 |
| issn | 0378-7206 1872-7530 |
| language | eng |
| recordid | cdi_proquest_journals_237019839 |
| source | Elsevier ScienceDirect Journals |
| subjects | Applied sciences Artificial intelligence Backpropagation algorithm Classification Computer science control theory systems Connectionism. Neural networks Discriminant analysis Exact sciences and technology Information and communication sciences Information and communication technologies Information science. Documentation Information technologies: storage media, equipment Information technology IT adoption Neural networks Sciences and techniques of general use Studies Symbolic rules Technical characteristics |
| title | Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT |
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