Abstract:

Aiming at the problem that the rough set cannot deal with the continuous attributes directly, combining with the rough set theory and the particle swarm algorithm, we propose a discretization algorithm based on adaptive hybrid tabu search particle swarm optimization. Firstly, the adaptive adjustment strategy is introduced, which cannot only overcome the problem of local extremum of the particle swarm, but also improve the abilities of seeking a global excellent result. In order to get the best global optimal particle, the tabu search is performed on the global optimal particle of each generation to enhance the local search ability of the particle swarm. Finally, under the premise of keeping the classification ability of the decision table, the attribute discretization points are initialized as a group of random particles. The algorithm searches for the best discretization points in the self iteration of the modified particle swarm. Experimental results show that compared with other discretization algorithms based on the rough set, the proposed algorithm not only has better classification accuracy and less discretization breakpoints, but also improves the discretization of continuous attributes.

Key words: rough set;particle swarm optimization;discretization;adaptive;tabu search