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Precision parameter in the variable precision rough sets model: an application

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  • Su, Chao-Ton
  • Hsu, Jyh-Hwa

Abstract

Despite their diverse applications in many domains, the variable precision rough sets (VPRS) model lacks a feasible method to determine a precision parameter ([beta]) value to control the choice of [beta]-reducts. In this study we propose an effective method to find the [beta]-reducts. First, we calculate a precision parameter value to find the subsets of information system that are based on the least upper bound of the data misclassification error. Next, we measure the quality of classification and remove redundant attributes from each subset. We use a simple example to explain this method and even a real-world example is analyzed. Comparing the implementation results from the proposed method with the neural network approach, our proposed method demonstrates a better performance.

Suggested Citation

  • Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
    • Handle: RePEc:eee:jomega:v:34:y:2006:i:2:p:149-157
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    References listed on IDEAS

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    1. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
    2. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    3. Beynon, Malcolm, 2001. "Reducts within the variable precision rough sets model: A further investigation," European Journal of Operational Research, Elsevier, vol. 134(3), pages 592-605, November.
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    Cited by:

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    2. Chen, Li-Fei & Tsai, Chih-Tsung, 2016. "Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain," Tourism Management, Elsevier, vol. 53(C), pages 197-206.
    3. Xiaoqing Li & Qingquan Jiang & Maxwell K. Hsu & Qinglan Chen, 2019. "Support or Risk? Software Project Risk Assessment Model Based on Rough Set Theory and Backpropagation Neural Network," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    4. Wenbing Chang & Xinglong Yuan & Yalong Wu & Shenghan Zhou & Jingsong Lei & Yiyong Xiao, 2019. "Decision-Making Method based on Mixed Integer Linear Programming and Rough Set: A Case Study of Diesel Engine Quality and Assembly Clearance Data," Sustainability, MDPI, vol. 11(3), pages 1-21, January.

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