In order to address the issue of test cost being rarely considered in rough set attribute reduction, this paper proposed a three-way neighborhood attribute reduction algorithm based on test cost. The proposed algorithm calculated the attri-bute importance according to the frequency and proportion of each attribute in the neighborhood resolution matrix, and combined the test cost of the attributes to construct the the cost performance index to guide the selection of attributes. Three-way decision-making method was employed to partition attribute sets, which provided data support for the attribute reduction process. Comparative experiments were conducted on seven UCI public datasets, which demonstrate that the proposed algorithm yields a smaller attribute reduction set compared to the comparison algorithm. Moreover, the proposed algorithm exhibited a shorter running time and lower test cost without compromising classification accuracy. Furthermore, it provided an application example based on fiscal revenue prediction to further validate the effectiveness and practicality of the proposed algorithm.

[2023-11-17] Accepted Paper /doc/2023.06.0306/v1

[2024-03-05] Printed Article /doc/2023.06.0306/v2