DocumentCode :
2623384
Title :
Discovering production rules with higher order neural networks: a case study. II
Author :
Kowalczyk, Adam ; Ferrá, Herman L. ; Gardiner, Ken
Author_Institution :
Telecom Australia Res. Lab., Clayton, Vic., Australia
fYear :
1991
fDate :
18-21 Nov 1991
Firstpage :
547
Abstract :
It is demonstrated by example that neural networks can be used successfully for automatic extraction of production rules from empirical data. The case considered is a popular public domain database of 8124 mushrooms. With the use of a term selection algorithm, a number of very accurate mask perceptrons (a kind of high-order network or polynomial classifier) have been developed. Then rounding of synaptic weights was applied, leading in many cases to networks with integer weights which were subsequently converted to production rules. It is also shown that focusing of network attention onto a smaller subset of useful attributes ordered with respect to their decreasing discriminating abilities helps significantly in accurate rule generation
Keywords :
biology computing; knowledge acquisition; knowledge based systems; neural nets; high-order neural networks; mask perceptrons; mushrooms; polynomial classifier; production rule extraction; public domain database; synaptic weight rounding; term selection algorithm; Artificial neural networks; Biological system modeling; Computer aided software engineering; Computer science; Laboratories; Mathematics; Neural networks; Polynomials; Production; Telecommunications;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
Type :
conf
DOI :
10.1109/IJCNN.1991.170457
Filename :
170457
Link To Document :
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