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Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes

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Abstract

Rough set theory is a new data mining approach to manage vagueness. It is capable to discover important facts hidden in the data. Literature indicate the current rough set based approaches can’t guarantee that classification of a decision table is credible and it is not able to generate robust decision rules when new attributes are incrementally added in. In this study, an incremental attribute oriented rule-extraction algorithm is proposed to solve this deficiency commonly observed in the literature related to decision rule induction. The proposed approach considers incremental attributes based on the alternative rule extraction algorithm (AREA), which was presented for discovering preference-based rules according to the reducts with the maximum of strength index (SI), specifically the case that the desired reducts are not necessarily unique since several reducts could include the same value of SI. Using the AREA, an alternative rule can be defined as the rule which holds identical preference to the original decision rule and may be more attractive to a decision-maker than the original one. Through implementing the proposed approach, it can be effectively operating with new attributes to be added in the database/information systems. It is not required to re-compute the updated data set similar to the first step at the initial stage. The proposed algorithm also excludes these repetitive rules during the solution search stage since most of the rule induction approaches generate the repetitive rules. The proposed approach is capable to efficiently and effectively generate the complete, robust and non-repetitive decision rules. The rules derived from the data set provide an indication of how to effectively study this problem in further investigations.

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Acknowledgements

This research was partially sponsored by the NSC in Taiwan (NSC 99-2410-H-260-051-MY3 and NSC 98-2410-H-260-011-MY3) and the US Department of Education (Award #P116B080100A).

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Authors and Affiliations

  1. Department of Information Management, National Chi Nan University, No. 1, University Road, Puli, Nantou, 545, Taiwan, ROC

    Chun-Che Huang & Chih-Hua Hsu

  2. Department of Industrial Manufacturing and Systems Engineering, The University of Texas at El Paso, 500 West University Avenue, El Paso, TX, 79968, USA

    Tzu-Liang (Bill) Tseng

  3. Institute of Nuclear Science and Technology, Sichuan Universit, No. 24, South Section 1, Yihuan Road, Chengdu, 610065, China

    Fuhua Jiang

  4. Department of Information Management, National Taiwan University, No. 1, Section 4, Roosevelt Rd., Taipei City, 106, Taiwan, ROC

    Yu-Neng Fan

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  1. Chun-Che Huang
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  2. Tzu-Liang (Bill) Tseng
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  3. Fuhua Jiang
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  4. Yu-Neng Fan
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  5. Chih-Hua Hsu
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Correspondence to Tzu-Liang (Bill) Tseng.

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Huang, CC., Tseng, TL.(., Jiang, F. et al. Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes. Ann Oper Res 216, 163–189 (2014). https://doi.org/10.1007/s10479-013-1352-1

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