[1]余桂兰,陈珂,左敬龙.基于云模型的并行蚁群-SVM分类方法[J].计算机技术与发展,2014,24(04):131-134.
　YU Gui-lan,CHEN Ke,ZUO Jing-long.Parallel Ant Colony-SVM Classification Method Based on Cloud Model[J].,2014,24(04):131-134.
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基于云模型的并行蚁群-SVM分类方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年04期
页码:
131-134
栏目:
智能、算法、系统工程
出版日期:
2014-04-30

文章信息/Info

Title:
Parallel Ant Colony-SVM Classification Method Based on Cloud Model
文章编号:
1673-629X（2014）04-0131-04
作者:
余桂兰; 陈珂; 左敬龙
广东石油化工学院 计算机与电子信息学院
Author(s):
YU Gui-lan; CHEN Ke; ZUO Jing-long
关键词:
云模型; 逆向云发生器; 并行蚁群算法; 支持向量机; 网页分类
Keywords:
cloud model; backward cloud generator; parallel ant colony algorithm; SVM; web page classification
分类号:
TP391
文献标志码:
A
摘要:
支持向量机( SVM)是一种高效的分类识别方法，在解决高维模式识别问题中表现出许多特有的优势，但SVM不利于海量数据的挖掘。为了改善SVM对大样本数据的适应性，提高算法的收敛速度，利用云模型来优化并行蚁群算法，提出了一种基于云模型的并行蚁群-SVM网页分类方法。将蚂蚁当前位置坐标作为云滴的两个参数，用逆向云发生器产生信息云的三个数字特征，采用不同的方法来更新蚂蚁的信息素，比较真实地体现了现实蚁群的运作情况，达到了实时动态更新的效果。通过对比测试，验证了CPACA-SVM方法在准确率和召回率上均有明显提高，具有较好的分类效果。
Abstract:
SVM is an effective method for learning the classification knowledge from massive data,especially in solving the high dimen-sional pattern recognition problem. But SVM is not conducive to massive data mining. To improve the adaptability of SVM on large-scale scenes and the speed of convergence of the algorithm,utilizing the cloud model to optimize parallel ant colony algorithm,a parallel ant colony-SVM web page classification method based on cloud model is proposed. Three digital characteristics of the cloud is produced from backward cloud generator,the ants current position coordinates is composed of the two parameters of cloud droplets. Using different meth-ods to update the ant pheromones,more accurately reflect the life of the ant colony,to achieve the effect of real-time dynamic updates. By comparison test verify the CPACA-SVM method on precision and recall rate significantly improved,with better classification effect.

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更新日期/Last Update: 1900-01-01