Ant colony classification mining algorithm based on pheromone attraction and exclusion

doi:10.1007/s00500-016-2151-9

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Ant colony classification mining algorithm based on pheromone attraction and exclusion
	Yang, Lei 1; Li, Kangshun 1; Zhang, Wensheng2 ; Ke, Zhenxu 1
刊名	SOFT COMPUTING
	2017-10-01
卷号	21 期号:19 页码:5741-5753
关键词	Data Mining Classification Rule Ant Colony Algorithm Pheromone Attraction And Exclusion
DOI	10.1007/s00500-016-2151-9
文献子类	Article
英文摘要	Ant colony optimization algorithms have been applied successfully in classification rule mining. However, basic ant colony classification mining algorithms generally suffer from problems, such as premature convergence and falling into local optimum easily. Simultaneously, the classification mining algorithms use sequential covering strategy to discover rules, and the interaction between rules is less considered. In this study, a new ant colony classification mining algorithm based on pheromone attraction and exclusion (Ant-Miner(PAE)) is proposed, in which a new pheromone calculation method is designed and the search is guided by the new probability transfer formula. By contrast, the basic algorithm structure is modified, and the order of the iteration is adjusted. Thus, the problem of rule interaction is mitigated. Ant-Miner(PAE) can balance the relation of exploration and development of constructing rules, which can make the ants in the search process initially explore and develop in the later period. Our experiments, which use 12 publicly available data sets, show that the predictive accuracy obtained by Ant-Miner(PAE) implementing the proposed pheromone attraction and exclusion strategy is statistically significantly higher than the predictive accuracy of other rule induction classification algorithms, such as CN2, C4.5 rules, PSO/AC-O2, Ant-Miner, and cAnt-Miner(PB). Furthermore, the rules discovered by Ant-Miner(PAE) are considerably simpler than those discovered by its counterparts.
WOS关键词	ILL-POSED PROBLEMS ; PARAMETER-IDENTIFICATION ; MULTIGRID METHOD ; OPTIMIZATION
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000411867700018
资助机构	Science and Technology Project of Guangdong Province of China(2015A020209119 ; National Natural Science Foundation of China(61573157) ; Natural Science Foundation of Guangdong Province of China(S2013040015755) ; 2014A020208087)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/20735]
专题	精密感知与控制研究中心_人工智能与机器学习
作者单位	1.South China Agr Univ, Coll Math & Informat, Guangzhou, Guangdong, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Li, Kangshun,Zhang, Wensheng,et al. Ant colony classification mining algorithm based on pheromone attraction and exclusion[J]. SOFT COMPUTING,2017,21(19):5741-5753.
APA	Yang, Lei,Li, Kangshun,Zhang, Wensheng,&Ke, Zhenxu.(2017).Ant colony classification mining algorithm based on pheromone attraction and exclusion.SOFT COMPUTING,21(19),5741-5753.
MLA	Yang, Lei,et al."Ant colony classification mining algorithm based on pheromone attraction and exclusion".SOFT COMPUTING 21.19(2017):5741-5753.