一种基于改进的权值调整技术数据源分类算法研究  

Classification of Deep Web data sources based on weight adjustment technique

作　　者：周晓庆[1] 肖顺文[1] 肖建琼[1] 罗兴贤[1] 

机构地区：[1]西华师范大学计算中心,四川南充637002

出　　处：《计算机应用研究》2012年第3期916-918,共3页Application Research of Computers

基　　金：四川省教育厅自然科学科研基金资助项目(10ZC016);西华师范大学科研项目(JGXM0950)

摘　　要：针对传统的搜索引擎无法正确搜索到Deep Web中隐藏的海量信息,对Web数据库的分类是通向Web数据库分类集成和检索的关键步骤。提出了一种基于权值调整技术的Deep Web数据库分类方法,首先从网页表单中提取特征;然后对这些特征使用一种新的权重计算方法进行估值;最后利用朴素贝叶斯分类器对Web数据库进行分类。实验表明,这种分类方法经过少量样本训练后,就能达到很好的分类效果,并且随着训练样本的增加,该分类器的性能保持稳定,准确率、召回率都在很小的范围内波动。The traditional search engine is unable to correct search for the magnanimous information in Deep Web hides. The Web database＇ s classification is the key step which integrates with the Web database classification and retrieves. This paper proposed a kind of classification of Deep Web data sources based on weight adjustment technique, which, used a new weight adjustment method to valuate the weight of feature extracted from the homepage form, and finally used the simple Bayes sorter to classify the Web database. The experiment indicates that after this taxonomic approach undergoes few sample training, it can achieve the very good classified effect, and along with training sample＇ s increase, this classifier＇ s performance maintains stable and the rate of accuracy and the recalling rate fluctuate in the very small scope.

关 键 词：WEB数据库 特征提取 特征估价 Nave BAYES分类器 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]