Cairo University, Faculty of Computers & Information Information Technology Department 5 Ahmed Zewal St., Orman, Giza, office box# 12613 (email)
In this paper, we propose a new attribute reduction strat-
egy 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 nding 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 promis-
ing attribute reduction technique. Like Genetic Algorithms, GWO is a
new evolutionary computation technique, mimics the leadership hierar-
chy and hunting mechanism of grey wolves in nature. The grey wolf
optimization nd 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 com-
putationally inexpensive in terms of both memory and runtime. Experi-
mentation 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 ecient for rough
set-based attribute reduction.