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Classification rule mining based on ant colony optimization algorithm
Jin P(金鹏); Zhu YL(朱云龙); Hu KY(胡琨元); Li SF(李素粉)
Department先进制造技术实验室
Conference NameInternational Conference on Intelligent Computing (ICIC)
Conference DateAugust 16-19, 2006
Conference PlaceKunming, China
Author of SourceIEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China
Source PublicationINTELLIGENT CONTROL AND AUTOMATION
PublisherSPRINGER-VERLAG
Publication PlaceBERLIN
2006
Pages654-663
Indexed BySCI ; CPCI(ISTP)
WOS IDWOS:000240383400082
Contribution Rank1
ISSN0170-8643
ISBN3-540-37255-5
AbstractClassification rule mining is an important function of data mining, and is applied in many data analysis tasks. The classification rule mining algorithm based-on ant colony optimization (ACO) is researched in this paper. Some improvements are implemented based on existing research to enhance classification predictive accuracy and simplicity of rules. Multi-population parallel strategy is proposed, the cost-based discretization method is adopted, and parameters in the algorithm are adjusted step by step. With these improvements, performance of the algorithm is advanced, and classification predictive accuracy is enhanced. Finally, SIMiner, a self-development data mining software system based on swarm intelligence, is applied to experiment on six data sets taken from UCI Repository on Machine Learning. The results illuminate the algorithm proposed in this paper has better performance in predictive accuracy and simplicity of rules.
Language英语
Citation statistics
Cited Times:11[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/8164
Collection工业信息学研究室_先进制造技术研究室
Affiliation1.Shenyang Institute of Automation of the Chinese Academy of Sciences
2.Graduate School of the Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jin P,Zhu YL,Hu KY,et al. Classification rule mining based on ant colony optimization algorithm[C]//IEEE Computat Intelligence Soc, Int Neural Network Soc, Natl Sci Fdn China. BERLIN:SPRINGER-VERLAG,2006:654-663.
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