基于改进离散粒子群优化的连续属性离散化

Discretization of continuous attributes based on improved discrete particle swarm optimization

为了解决数据挖掘和机器学习领域中连续属性离散化问题,提出一种改进的自适应离散粒子群优化算法。将连续属性的断点集合作为离散粒子群,通过粒子间的相互作用最小化断点子集,同时引入模拟退火算法作为局部搜索策略,提高了粒子群的多样性和寻找全局最优解的能力。利用粗糙集理论中决策属性对条件属性的依赖度来衡量决策表的一致性,从而达到连续属性离散化的目的,最后采用多组数据对此算法的性能进行了检验,并与其他算法做了对比实验,实验结果表明此算法是有效的。

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