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Inductive learning with a computational network
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DOI：10.1023/A:1007977204827.png)
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Abstract
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This paper introduces a computational network which combines heterogeneous rule-extraction algorithms for intelligent data analysis. Combining induction programs may alleviate the possible negative effects of data set representation and individual program's influences, such as inductive bias. The application of the computational network to a diabetes data set shows that, when combining the various programs, an increase in rule set accuracy and comprehensibility are obtained.
Keywords:
induction programs
inductive bias
computational network
diabetes
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