Support Vector Machines Analysis of Web Pages Binary Classification Quality
Abstract
This paper presents the analysis of classification quality of web-pages binary classification by the support vector machines method. This classification is required to reveal the web pages containing text information, which distribution is forbidden in Russian Federation. Results are shown for three document collections: “drug dealing”, “extremism” and “terrorism”. Collections of documents are created as a result of specialists’ work with one of the Internet information search and analysis information systems. For each collection, we describe class proportions of testing and training samples, distribution by the type of Internet resources, and several problems that make the classification itself or classifier training difficult. Formation of document’s vector is also described. Next, we show testing results for different kernel functions and analyze classification mistakes. We use precision, recall and F1 score as quality measures. Machine learning library “scikit-learn” is used to implement support vector machines. Finally, we make assumptions about classification quality.
DOI 10.14258/izvasu(2017)4-14
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Copyright (c) 2017 С.В. Волошин, А.Л. Царегородцев, Е.А. Карташев, В.В. Славский
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