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CLC number: TP391

On-line Access: 2020-07-10

Received: 2019-05-13

Revision Accepted: 2019-08-21

Crosschecked: 2020-06-06

Cited: 0

Clicked: 448

Citations:  Bibtex RefMan EndNote GB/T7714


Li Deng


Xin Du


Ji-zhong Shen


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.995-1004


Web page classification based on heterogeneous features and a combination of multiple classifiers

Author(s):  Li Deng, Xin Du, Ji-zhong Shen

Affiliation(s):  College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   jzshen@zju.edu.cn

Key Words:  Web page classification, Web page features, Combined classifiers

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.

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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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