Generating concise and accurate classification rules for breast cancer diagnosis.
In our previous work, we have presented an algorithm that extracts classification rules from trained neural networks and discussed its application to breast cancer diagnosis. In this paper, we describe how the accuracy of the networks and the accuracy of the rules extracted from them can be improved by a simple pre-processing of the data. Data pre-processing involves selecting the relevant input attributes and removing those samples with missing attribute values. The rules generated by our neural network rule extraction algorithm are more concise and accurate than those generated by other rule generating methods reported in the literature.
Setiono
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School of Computing, National University of Singapore, Kent Ridge, Singapore, Singapore. rudys@comp.nus.edu.sg
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer