A bi-variable precision rough set model and its application to attribute reduction

Abstract

Significant indicators for measuring algorithms for attribute reduction include their fault tolerance. The majority of existing algorithms rely on the incorporation of fault tolerance at the upper and lower approximation operators. However, information granules partitioned by these algorithms are not inherently fault-tolerant. To enhance the fault tolerance of attribute reduction algorithms, this study introduces a new model based on variable precision rough sets, comprising of three parameters: the inclusion of fault tolerance in the comparison of attribute values between two objects, the partition of information granules, and the calculation of upper and lower approximation operators. As a result, the model tolerates errors, enhances the fault-tolerance ability of information granules, and improves its inclusivity. Using this new model, we design a novel algorithm that deletes both redundant and noise attributes to improve the classification accuracy. Through analysis of classification accuracy and robustness, we confirm that the novel algorithm's fault tolerance and robustness are maintained even after adding random Gaussian noise. The parameters included as part of the model are also found to be mostly unchanged after the addition of random Gaussian noise.