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Efficient Algorithms for Dynamic Incomplete Decision Systems

Author

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  • Nguyen Truong Thang

    (Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam)

  • Giang Long Nguyen

    (Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam)

  • Hoang Viet Long

    (People's Police University of Technology and Logistics, Vietnam)

  • Nguyen Anh Tuan

    (VinhPhuc College, Vietnam)

  • Tuan Manh Tran

    (Thuyloi University, Vietnam)

  • Ngo Duy Tan

    (Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam)

Abstract

Attribute reduction is a crucial problem in the process of data mining and knowledge discovery in big data. In incomplete decision systems, the model using tolerance rough set is fundamental to solve the problem by computing the redact to reduce the execution time. However, these proposals used the traditional filter approach so that the reduct was not optimal in the number of attributes and the accuracy of classification. The problem is critical in the dynamic incomplete decision systems which are more appropriate for real-world applications. Therefore, this paper proposes two novel incremental algorithms using the combination of filter and wrapper approach, namely IFWA_ADO and IFWA_DEO, respectively, for the dynamic incomplete decision systems. The IFWA_ADO computes reduct incrementally in cases of adding multiple objects while IFWA_DEO updates reduct when removing multiple objects. These algorithms are also verified on six data sets. Experimental results show that the filter-wrapper algorithms get higher performance than the other filter incremental algorithms.

Suggested Citation

  • Nguyen Truong Thang & Giang Long Nguyen & Hoang Viet Long & Nguyen Anh Tuan & Tuan Manh Tran & Ngo Duy Tan, 2021. "Efficient Algorithms for Dynamic Incomplete Decision Systems," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 17(3), pages 44-67, July.
    • Handle: RePEc:igg:jdwm00:v:17:y:2021:i:3:p:44-67
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