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
收藏
页码：95 / 106
页数：12
相关论文
共 50 条
  • [1] Parallel ant colony algorithm for mining classification rules
    Chen, Yixin
    Chen, Ling
    Tu, Li
    [J]. 2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, 2006, : 85 - +
  • [2] Parallel mining for classification rules with ant colony algorithm
    Chen, L
    Tu, L
    [J]. COMPUTATIONAL INTELLIGENCE AND SECURITY, PT 1, PROCEEDINGS, 2005, 3801 : 261 - 266
  • [3] Classification rule mining with an improved ant colony algorithm
    Wang, ZQ
    Feng, BQ
    [J]. AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2004, 3339 : 357 - 367
  • [4] An Adaptive Ant Colony Algorithm for Classification Rule Mining
    Zhang, Xiaomeng
    Sun, Wensheng
    [J]. PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INDUSTRIAL ENGINEERING (AIIE 2016), 2016, 133 : 295 - 299
  • [5] Classification rule mining based on ant colony optimization algorithm
    Jin, Peng
    Zhu, Yunlong
    Hu, Kunyuan
    Li, Sufen
    [J]. INTELLIGENT CONTROL AND AUTOMATION, 2006, 344 : 654 - 663
  • [6] Rule mining algorithm with a new ant colony optimization algorithm
    Thangavel, K.
    Jaganathan, P.
    [J]. ICCIMA 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, VOL II, PROCEEDINGS, 2007, : 135 - +
  • [7] FCACO: Fuzzy classification rules mining algorithm with ant colony optimization
    Alatas, B
    Akin, E
    [J]. ADVANCES IN NATURAL COMPUTATION, PT 3, PROCEEDINGS, 2005, 3612 : 787 - 797
  • [8] Ant colony classification mining algorithm based on pheromone attraction and exclusion
    Lei Yang
    Kangshun Li
    Wensheng Zhang
    Zhenxu Ke
    [J]. Soft Computing, 2017, 21 : 5741 - 5753
  • [9] Ant colony classification mining algorithm based on pheromone attraction and exclusion
    Yang, Lei
    Li, Kangshun
    Zhang, Wensheng
    Ke, Zhenxu
    [J]. SOFT COMPUTING, 2017, 21 (19) : 5741 - 5753
  • [10] An adaptive ant colony classification algorithm
    Ma, An-Xiang
    Zhang, Chang-Sheng
    Zhang, Bin
    Zhang, Xiao-Hong
    [J]. Zhang, Bin, 1600, Northeast University (35): : 1102 - 1106
12345