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Extracting rules from pruned networks for breast cancer diagnosis.

Abstract：

:A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of hidden unit activation values. The accuracy of the extracted rules is as high as the accuracy of the pruned network. For the breast cancer diagnosis problem, the concise rules extracted from the network achieve an accuracy rate of more than 95% on the training data set and on the test data set.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Setiono R

doi

10.1016/0933-3657(95)00019-4

subject

Has Abstract

pub_date

1996-02-01 00:00:00

pages

37-51

issue

1

eissn

0933-3657

issn

1873-2860

pii

0933365795000194

journal_volume

8

pub_type

杂志文章

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