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Frontiers of Information Technology & Electronic Engineering >> 2020, Volume 21, Issue 7 doi: 10.1631/FITEE.1900240

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

浙江大学信息与电子工程学院,中国杭州市,310027

Received: 2019-05-13 Accepted: 2020-07-10 Available online: 2020-07-10

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Abstract

Precise 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 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 is improved by combining multiple classifiers, and is higher than those of the related algorithms.

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