Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0933-3657(99)00041-X
Title: Generating concise and accurate classification rules for breast cancer diagnosis
Authors: Setiono, R. 
Keywords: Attribute selection
Data pre-processing
Neural network rule extraction
Wisconsin breast cancer diagnosis
Issue Date: 2000
Citation: Setiono, R. (2000). Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine 18 (3) : 205-219. ScholarBank@NUS Repository. https://doi.org/10.1016/S0933-3657(99)00041-X
Abstract: 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. (C) 2000 Elsevier Science B.V.
Source Title: Artificial Intelligence in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/42371
ISSN: 09333657
DOI: 10.1016/S0933-3657(99)00041-X
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