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Knowledge extraction from neural networks

By Antony Browne, Brian Hudson, David Whitley, Martyn Ford, Phil Picton and Hassan Kazemian


<p>In the past, neural networks have been viewed as classification and regression systems whose internal representations were incomprehensible. It is now becoming apparent that algorithms can be designed which extract comprehensible representations from trained neural networks, enabling them to be used for data mining, i.e. the discovery and explanation of previously unknown relationships present in data. This paper reviews existing algorithms for extracting comprehensible representations from neural networks and describes research to generalize and extend the capabilities of one of these algorithms. The algorithm has been generalized for application to bioinformatics datasets, including the prediction of splice site junctions in human DNA sequences. Results generated on this dataset are compared with those generated by a conventional data mining technique (C5), and conclusions are drawn regarding the application of the neural network based technique to other fields of interest.</p

Year: 2003
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