Abstract
Less of comprehensibility is the major drawback of neural network. It is considered that rule extraction from neural network is the main method to reveal the ’black-box’. From this point, the comprehensibility of the rules extracted is very important. If not so, rule extraction is unmeaning. For problems with continuous-valued and discrete-valued attributes, there are few existing algorithms to process. This paper presents two ideas to resolve the problems with continuous-valued and discrete-valued attributes. Rules extracted are comprehensible not only for discrete value but also for continuous value. The two methods have its respective advantages and disadvantages and are both validated by experiment results on real-world dataset.
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© 2007 Springer-Verlag Berlin Heidelberg
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Guo, P., Chen, J., Sun, S. (2007). Two Approaches to Extract Rules from Neural Network with Mixed Attributes. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_7
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DOI: https://doi.org/10.1007/978-3-540-74282-1_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
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