Rough sets for adapting wavelet neural networks as a new classifier system |
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Authors: | Yasser F Hassan |
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Affiliation: | 1.Department of Mathematics (Computer Science),Alexandria University,Alexandria,Egypt |
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Abstract: | Classification is an important theme in data mining. Rough sets and neural networks are two techniques applied to data mining
problems. Wavelet neural networks have recently attracted great interest because of their advantages over conventional neural
networks as they are universal approximations and achieve faster convergence. This paper presents a hybrid system to extract
efficiently classification rules from decision table. The neurons of such hybrid network instantiate approximate reasoning
knowledge gleaned from input data. The new model uses rough set theory to help in decreasing the computational effort needed
for building the network structure by using what is called reduct algorithm and a rules set (knowledge) is generated from
the decision table. By applying the wavelets, frequencies analysis, rough sets and dynamic scaling in connection with neural
network, novel and reliable classifier architecture is obtained and its effectiveness is verified by the experiments comparing
with traditional rough set and neural networks approaches. |
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