Hierarchical Contaminated Web Page Classification Based on Meta Tag Denoising Disposal

Abstract

Web page classification is critical for information retrieval. Most web page classification methods have the following two faults: (1) need to analyze based on the overall web page and (2) do not pay enough attention to the existence of noise information inside the web page, which will thus decrease the efficiency and classification performance, especially when classifying the contaminated web page. To solve these problems, this paper proposes a denoising disposal algorithm. We choose the top-down method for hierarchical classification to improve the prediction efficiency. The experimental results demonstrate that our method is about 7 times faster than the full-page method and achieves good classification results in most categories. The precision of 7 parent categories is all above 88% and is 24% higher than the other meta tag-based method on average.

Web Page Classification Algorithm Based on Deep Learning

“…WPC research based on DL is crucial to applications such as directory website maintenance, topic browser construction, and search engine user experience improvement [ 1 , 2 ]. One version that web users often appear in web page classifications is that portals generally display news at different locations on the homepage of technology and finance [ 3 , 4 ]. Large-scale news makes manual classification unrealistic.…”