A New Ant Colony Classification Mining Algorithm

被引：0

作者：

Yang, Lei ^{[1
]}

Li, Kangshun ^{[1
]}

Zhang, Wensheng ^{[2
]}

Chen, Yan ^{[1
]}

Li, Wei ^{[1
]}

Bi, Xinghao ^{[1
]}

机构：

[1] South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Guangdong, Peoples R China

[2] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS, (ISICA 2015) | 2016年 / 575卷

关键词：

Data mining; Ant colony algorithm; Classification rule; Pheromone; OPTIMIZATION;

D O I：

10.1007/978-981-10-0356-1_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ant colony optimization algorithms have been successfully applied in classification rule mining, but in general, the basic ant colony classification mining algorithms have the problems of premature convergence, easily falling into local optimum, and etc. In this paper, a new ant colony classification mining algorithm based on pheromone attraction and exclusion (Ant-MinerPAE) is proposed, where the new pheromone calculation method is designed and the search is guided by the new probability transfer formula. Our experiments using 12 publicly available data sets show that the predictive accuracy obtained by the Ant-MinerPAE algorithm is statistically significantly higher than the predictive accuracy of other rule induction classification algorithms, such as CN2, C4.5rules, PSO/ACO2, Ant-Miner, CAnt-MinerPB, and the rules discovered by the Ant-MinerPAE algorithm are considerably simpler than those discovered by the counterparts.

引用

页码：95 / 106

页数：12

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