Rough Sets Based Approach to Reduct Approximation: RSBARA

Attribute reduction is the process of choosing a subset of attributes from the original set of attributes forming patterns in a given dataset. The subset should be necessary and sufficient to describe target concepts. Rough set theory has been used as an attribute reduction method with impressive success, but current methods are inadequate at finding optimal reductions. On the other hand, the optimal reducts can be obtained by using the stochastic approaches, but it is not easy because its computational complexity for computing reducts is at least O [NtimesM²], where N is the number of attributes and M is the total number of objects. In this paper, we propose an algorithm which uses rough set theory to approximate the reduct and reduces the required computational effort to O[N²timesM]. Experimentation is carried out, using UCI data, which compares with a particle swarm approach and other deterministic rough set reduction algorithms. The experimental results show that the purposed method is more efficient both accuracy and attribute reduction.