Rough Set-Based Dataset Reduction Method Using Swarm Algorithm and Cluster Validation Function

A Rough Set (RS) based dataset reduction method using SWARM optimization algorithm and a cluster validation function is proposed. In the proposed approach, the user specifies the classification quality required in advance, and the method then finds the attribute reducts and perform attribute discretization to satisfy the desired quality of classification. While many other solutions are possible, the proposed method yields the solution which satisfies the optimal discretization conditions by means of a newly-designed cluster validation index function. The performance of the proposed method is compared with that of two existing attribute reduction and classification methods for eight benchmark datasets. The results confirm that the proposed method provides an effective tool for solving simultaneous attribute reduction and discretization problems.