摘要
Abstract
Aiming at the problem of slow convergence speed and poor reduction results when the social spider optimization algorithm solves the minimum attribute reduction.This paper proposed a minimum attribute reduction algorithm based on triple restraints social spider optimiza-tion(TRSSOAR).Constrain the individuals in the population during the initialization stage,during the iteration process and at the end of the iteration respectively.First,a fitness voting strategy is proposed to optimize the initial state of the population so that most individuals in the population are in a good position;Then,in the iterative process,opposition-based learning is introduced,and a local opposition-based learn-ing strategy is designed to improve the individual quality of the population and expand the search space;Thirdly,in order to obtain fewer re-duction results,a redundancy detection strategy is used to remove redundant attributes in the reduction results;finally,experiments are con-ducted on nine UCI data sets and compared with four representative algorithms.The results show that the proposed algorithm performs well in terms of reduction capability,running time and convergence speed,and has certain advantages in solving the minimum attribute reduction problem.关键词
粗糙集/最小属性约简/社会蜘蛛优化/对立学习/冗余检测Key words
rough sets/minimum attribute reduction/social spider optimization/opposition-based learning/redundancy detection分类
信息技术与安全科学
