计算机工程与应用2017，Vol.53Issue(18)：108-114,235,8.DOI:10.3778/j.issn.1002-8331.1603-0307
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
摘要
Abstract
In order to solve the problem of data mining and the discretization of continuous attributes in the field of ma-chine 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 inter-action 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, achiev-ing 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.关键词
离散粒子群/模拟退火/粗糙集/连续属性离散化Key words
discrete particle swarm/simulated annealing/rough set/continuous attributes discretization分类
信息技术与安全科学引用本文复制引用
张荣光,胡晓辉,宗永胜..基于改进离散粒子群优化的连续属性离散化[J].计算机工程与应用,2017,53(18):108-114,235,8.基金项目
国家自然科学基金(No.61163009) （No.61163009）
甘肃省科技支撑计划项目(No.144NKCA040). （No.144NKCA040）
