Aiding Fuzzy Rule Induction with Fuzzy Rough Attribute Reduction

نویسندگان

  • Richard Jensen
  • Qiang Shen
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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تاریخ انتشار 2002