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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46122

    Title: Extracting Rules from Composite Neural Networks for Medical Diagnostic Problems
    Authors: 蘇木春;Su, Mu-chun;Chang, Hsiao-te
    Contributors: 淡江大學電機工程學系
    Keywords: expert system;genetic algorithms;medical diagnosis;neural networks;rule extraction
    Date: 1998-12-01
    Issue Date: 2010-03-26 20:59:55 (UTC+8)
    Publisher: New York: Springer New York LLC
    Abstract: Recently, neural networks have been applied to many medical diagnostic problems because of their appealing properties, robustness, capability of generalization and fault tolerance. Although the predictive accuracy of neural networks may be higher than that of traditional methods (e.g., statistical methods) or human experts, the lack of explanation from a trained neural network leads to the difficulty that users would hesitate to take the advise of a black box on faith alone. This paper presents a class of composite neural networks which are trained in such a way that the values of the network parameters can be utilized to generate If-Then rules on the basis of preselected meaningful coordinates. The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.
    Relation: Neural Processing Letters 8(3), pp.253-263
    DOI: 10.1023/A:1009681803460
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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