Abstract
Much effort has been spent during recent years to develop techniques for rule extraction from opaque models, typically trained neural networks. A rule extraction technique could use different strategies for the extraction phase, either a local or a global strategy. The main contribution of this paper is the suggestion of a novel rule extraction method, called Cluster and See Through (CaST), based on the global strategy. CaST uses parts of the well-known RX algorithm, which is based on the local strategy, but in a slightly modified way. The novel method is evaluated against RX and is shown to get as good as or better results on all problems evaluated, with much more compact rules.
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Löfström, T., Johansson, U., Niklasson, L. (2004). Rule Extraction by Seeing Through the Model. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_85
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DOI: https://doi.org/10.1007/978-3-540-30499-9_85
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