New Rough Set Attribute Reduction Algorithm Based on Grey Wolf Optimization

In this paper, we propose a new attribute reduction strategy based on rough sets and grey wolf optimization (GWO). Rough sets have been used as an attribute reduction technique with much success, but current hill-climbing rough set approaches to attribute reduction are inconvenient at finding optimal reductions as no perfect heuristic can guarantee optimality. Otherwise, complete searches are not feasible for even medium sized datasets. So, stochastic approaches provide a promising attribute reduction technique. Like Genetic Algorithms, GWO is a new evolutionary computation technique, mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. The grey wolf optimization find optimal regions of the complex search space through the interaction of individuals in the population. Compared with GAs, GWO does not need complex operators such as crossover and mutation, it requires only primitive and easy mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that GWO is efficient for rough set-based attribute reduction.