关键词: 离散粒子群, 模拟退火, 粗糙集, 连续属性离散化

Abstract: In order to solve the problem of data mining and the discretization of continuous attributes in the field of machine learning, an improved adaptive discrete particle swarm optimization algorithm is proposed. This method treats the discrete particle swarm as a breakpoint set of continuous attributes. It also minimizes breakpoint subset through the interaction of particles, combined with simulated annealing algorithm as a partial search strategy for particles, enriching the particle swarm and enhancing the ability to look for the whole optimal solution. In addition, the consistency of decision table is measured according to the dependence of decision attribute in the rough set theory on condition attribute, achieving the goal of continuous attributes discretization. Finally the performance of this algorithm is tested through multiple sets of data and compared with other algorithms through experiments. As the results show, this algorithm is effective.

Key words: discrete particle swarm, simulated annealing, rough set, continuous attributes discretization