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计算机工程 ›› 2008, Vol. 34 ›› Issue (11): 86-87,9. doi: 10.3969/j.issn.1000-3428.2008.11.031

• 软件技术与数据库 • 上一篇    下一篇

一种基于混合算法的分类器设计

邝艳敏,王自强,李 鹏   

  1. (河南工业大学信息科学与工程学院,郑州 450001)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-06-05 发布日期:2008-06-05

Design of Classifier Based on Hybrid Algorithm

KUANG Yan-min, WANG Zi-qiang, LI Peng   

  1. (College of Information Science and Enginering, Henan University of Technology, Zhengzhou 450001)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-06-05 Published:2008-06-05

摘要: 为了高效地从数据库中挖掘分类规则,提出一种将粒子群优化算法和遗传算法相结合的新算法。该算法的核心思想是对规则的前件进行固定长度编码,适应度函数的计算由分类规则的准确率、置信度、支持度和简洁度构成,从而实现基于两者混合算法的分类器设计。将该分类器与遗传算法分类器和粒子群算法分类器进行对比,实验结果表明,该分类器具有更高的分类准确率以及更快的收敛速度。

关键词: 数据挖掘, 粒子群, 遗传算法, 分类器, 分类规则

Abstract: To efficiently mine the classification rule from database, a novel hybrid classification algorithm based on Particle Swarm Optimization(PSO) and Genetic Algorithm(GA) is proposed. The core idea of the proposed algorithm is as follows: a new rule code with fixed length is proposed, a novel fitness function combined with accuracy, confidence, support and simplicity is constructed, and a hybrid heuristic classifier is accomplished. Experimental results show that the proposed classification algorithm acheives higher classification accuracy and lower running time compared with other classification algorithms.

Key words: data mining, particle swarm, genetic algorithm, classifier, classification rule

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