Web Page Classification Based on SVM

Xue, Weimin; Bao, Hong; Huang, Weitong; Lu, Yuchang

doi:10.1109/wcica.2006.1714255

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Using confidence, the classification results of different classifiers can be compared. Different types of classifiers are combined to make full use of their advantages; i.e., deep neural network has the ability to extract high-level features from a large amount of raw data (Sze et al, 2017), and SVM is widely used for text categorization because of its high generalization performance and high tolerance ability of processing high-dimensional vector classification (Xue et al, 2006). The combined classifiers are an LSTM classifier based on fusion of textual and structural features and an SVM classifier based on fusion of textual and structural features.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

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

Deng

Shen

2020

Front Inform Technol Electron Eng

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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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Section: Experimental Results and Analysismentioning

confidence: 99%

“…The classifiers we use are the long short-term memory (LSTM) (Gers et al, 2000) network and SVM (Xue et al, 2006). LSTM is a special type of deep neural network, which has internal memory to allow long-term dependencies to affect the output (Sze et al, 2017).…”

Section: Fusion Of Features For Training and Classificationmentioning

confidence: 99%

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

Deng

Shen

2020

Front Inform Technol Electron Eng

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“…Formally the SVM constructs the optimal hyperplane under the condition of linearly separable. SVMs are very popular in text and web classification [10] due to the good results that can be achieved using them.…”

Section: Related Workmentioning

confidence: 99%

Implementing Web Classification for TLDs

Deri

Martinelli

Sartiano

et al. 2015

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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On the market there are many commercial web classification services and a few publicly available web directory services. Unfortunately they mostly focus on English-speaking web sites, making them unsuitable for other languages in terms of classification reliability and coverage. This paper covers the design and implementation of a web-based classification tool for TLDs (Top Level Domain). Each domain is classified by analysing the main domain web site, and organised itin categories according to its content. The tool has been successfully validated by classifying all the registered .it Internet domains, whose results are presented in this paper.

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A Comparative Analysis on Artificial Intelligence Techniques for Web Phishing Classification

Rashid

Sallim

Noor

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Over the last few years, the web has been expanded to serve millions of users for various purposes all over the world. The web content filtering is essential to filter offensive, unwanted web content from web pages, reduced inappropriate content to prevent access to content which could compromise the network and spread malware. It also to tightened network security where web content filtering adds a much-needed layer of security to the network by blocking access to sites that raise an alarm. However, there are lack of comparison between classification techniques in previous studies in order to find the best classifier for the web page classification and the analysis related to it. Thus, the purpose of this study was to apply web page classification techniques and their performances is compared as it is the initial step in data mining before going to web filtering. In this project, three classifiers called Artificial Neural Network, J48 Decision Tree and Support Vector Machine were used to web phishing dataset in order to find the best possible classifier with small computational efforts that will give the best result in classifying the web page.

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Web Page Classification Based on SVM

Cited by 9 publications

References 9 publications

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

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

Implementing Web Classification for TLDs

A Comparative Analysis on Artificial Intelligence Techniques for Web Phishing Classification

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