New use of the HITS algorithm for fast web page classification

Abstract

Abstract:The immense number of documents published on the web requires the utilization of automatic classifiers that allow organizing and obtaining information from these large resources. Typically, automatic web pages classifiers handle millions of web pages, tens of thousands of features, and hundreds of categories. Most of the classifiers use the vector space model to represent the dataset of web pages. The components of each vector are computed using the term frequency inversed document frequency (TFIDF) scheme. Unfortunately, TFIDF-based classifiers face the problem of the large-scale size of input data that leads to a long processing time and an increase in resource requests. Therefore, there is an increasing demand to alleviate these problems by reducing the size of the input data without influencing the classification results. In this paper, we propose a novel approach that improves web page classifiers by reducing the size of the input data (i.e. web pages and feature reduction) by using the hypertext induced topic search (HITS) algorithm.We employ HITS results for weighting remaining features. We evaluate the performance of the proposed approach by comparing it with the TFIDF-based classifier. We demonstrate that our approach significantly reduces the time needed for classification.

Research on the joint fluctuation laws between locational marginal price and renewables based on complex networks: A case study in Independent System Operator New England

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The Research Trends of Text Classification Studies (2000–2020): A Bibliometric Analysis

“…Meanwhile, TC techniques have been applied in various contexts such as web page classification, authorship attribution, knowledge management, and spam email detection. For example, Qi and Davison (2009), Kiziloluk and Ozer (2017), and Meadi et al (2017) explored algorithms and features in web page classification; Li et al (2017) and Saleh et al (2017) focused on the application of semantics-based approaches in web page classification. In the field of authorship attribution, in addition to traditional unsupervised methods such as Burrows’ delta (Burrows, 2002), an increasing number of studies have employed machine learning based classification techniques and reported promising results (Ebrahimpour et al, 2013; Jockers et al, 2008; Posadasduran et al, 2017; Tsimboukakis & Tambouratzis, 2010).…”