摘要
随着海量网页信息的出现,网页分类已经成为数据挖掘领域的一个重要研究方向,网页分类是一种快速有效利用海量网页信息的重要技术.为了克服支持向量机进行网页分类时存在训练收敛较慢和分类精度不高的缺点,将改进的量子粒子群优化算法与支持向量机相结合,提出了一种融合改进量子粒子群算法和支持向量机的网页分类方法.首先引入柯西分布改进量子粒子群优化算法,其次利用改进的量子粒子群算法优化支持向量机的参数选择,然后利用支持向量机进行网页分类.实验结果表明,该方法具有较高的准确率、召回率和F1测试值,网页分类效率也得到了一定程度的提高.
With the emergence of the ,cast amounts of information on the web pages, web page classification has become an important research direction for the field of data mining, it is important technology for a fast and effective use of vast amounts of information on the web pages. In order to overcome slow rate of convergence and low accuracy of web page classification based on support vector machine, the improved quantum -behaved particle swarm optimization and support vector machine was combineal, and a web classification method was present based on improved quantum - behaved particle swarm optimization and support vector machine. First, cauchy distribution was used to improve the quantum particle swarm algorithm. Secondly, the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine. Finally, the support vector machine was used to classify the web pages. The experimental results show that the method has high accuracy, recall and F1 - Measure, and also improve the efficiency of web page classification.
出处
《湖南科技大学学报（自然科学版）》
CAS
北大核心
2012年第3期81-85,共5页
Journal of Hunan University of Science And Technology：Natural Science Edition
基金
广东省自然科学基金资助项目(S2011010003667)
关键词
量子粒子群算法
支持向量机
网页分类
柯西分布
quantum -behaved particle swarm optimization
support vector machine
web pagesclassification
cauchy distribution
