基于异构特征和组合分类器的网页分类

邓立; 杜歆; 沈继忠

doi:10.1631/FITEE.1900240

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基于异构特征和组合分类器的网页分类

常规文章 | Updated：2022-05-19

- 基于异构特征和组合分类器的网页分类
- Web page classification based on heterogeneous features and a combination of multiple classifiers
- 信息与电子工程前沿（英文） 2020年21卷第7期页码：995-1004
- Affiliations：
  
  College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Ji-zhong SHEN, E-mail: jzshen@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61471314) and the Welfare Technology Research Project of Zhejiang Province, China (No. LGG18F010003)
- DOI：10.1631/FITEE.1900240
  中图分类号： TP391
- 收稿：2019-05-13，
  
  修回：2020-;6-;6，
  
  纸质出版：2020-07
- Accepted：
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邓立, 杜歆, 沈继忠. 基于异构特征和组合分类器的网页分类[J]. 信息与电子工程前沿（英文）, 2020,21(7):995-1004.

Li DENG, Xin DU, Ji-zhong SHEN. Web page classification based on heterogeneous features and a combination of multiple classifiers[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 995-1004.
邓立, 杜歆, 沈继忠. 基于异构特征和组合分类器的网页分类[J]. 信息与电子工程前沿（英文）, 2020,21(7):995-1004. DOI： 10.1631/FITEE.1900240.

Li DENG, Xin DU, Ji-zhong SHEN. Web page classification based on heterogeneous features and a combination of multiple classifiers[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 995-1004. DOI： 10.1631/FITEE.1900240.

摘要

网页特征是网页分类的关键，通过有区分度的特征能有效对网页分类。网页结构特征是对文本特征的有效补充。不同分类器有不同特点，多分类器组合可实现分类器性能互补。提出一种基于异构特征和组合分类器的网页分类算法。与计算HTML标记的频率不同，本文采用树状分布的HTML标签表示网页结构特征，以向量形式将异构文本和结构特征融合。通过计算一组样本的分类准确率，提出将分类结果置信度作为比较不同分类器分类结果的标准。基于置信度采用投票、比较大小和直接输出的决策策略，得到组合分类器的分类结果。实验结果表明，在Amazon数据集、7-web-genres数据集和DMOZ数据集中，准确率分别提高到94.2%、95.4%、95.7%。融合文本和结构特征的分类方法比仅使用文本特征的方法更全面有效。同时多分类器组合能够提高网页分类准确率，高于同类网页组合分类算法。

Abstract

Precise web page classification can be achieved by evaluating features of web pages

and the structural features of web pages are effective complements to their textual features. Various classifiers have different characteristics

and multiple classifiers can be combined to allow classifiers to complement one another. In this study

a web page classification method based on heterogeneous features and a combination of multiple classifiers is proposed. Different from computing the frequency of HTML tags

we exploit the tree-like structure of HTML tags to characterize the structural features of a web page. Heterogeneous textual features and the proposed tree-like structural features are converted into vectors and fused. Confidence is proposed here as a criterion to compare the classification results of different classifiers by calculating the classification accuracy of a set of samples. Multiple classifiers are combined based on confidence with different decision strategies

such as voting

confidence comparison

and direct output

to give the final classification results. Experimental results demonstrate that on the Amazon dataset

7-web-genres dataset

and DMOZ dataset

the accuracies are increased to 94.2%

95.4%

and 95.7%

respectively. The fusion of the textual features with the proposed structural features is a comprehensive approach

and the accuracy is higher than that when using only textual features. At the same time

the accuracy of the web page classification is improved by combining multiple classifiers

and is higher than those of the related web page classification algorithms.

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