Aiding Fuzzy Rule Induction with Fuzzy Rough Attribute Reduction
نویسندگان
چکیده
Many rule induction algorithms are unable to cope with high dimensional descriptions of input features. To enable such techniques to be effective, a redundancy-removing step is usually carried out beforehand. Rough Set Theory (RST) has been used as such a dataset pre-processor with much success, however it is reliant upon a crisp dataset; important information may be lost as a result of quantization. By using fuzzy-rough sets this loss is avoided, allowing the reduction of noisy, real-valued attributes. This paper demonstrates the applicability of fuzzy-rough attribute reduction to the problem of learning classifiers, resulting in simpler rules with little loss in classification accuracy.
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