Artificial Immune System Based Web Page Classification

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dc.contributor.authorOnan, A
dc.date.accessioned2024-07-18T11:40:03Z
dc.date.available2024-07-18T11:40:03Z
dc.description.abstractAutomated classification of web pages is an important research direction in web mining, which aims to construct a classification model that can classify new instances based on labeled web documents. Machine learning algorithms are adapted to textual classification problems, including web document classification. Artificial immune systems are a branch of computational intelligence inspired by biological immune systems which is utilized to solve a variety of computational problems, including classification. This paper examines the effectiveness and suitability of artificial immune system based approaches for web page classification. Hence, two artificial immune system based classification algorithms, namely Immunos-1 and Immunos-99 algorithms are compared to two standard machine learning techniques, namely C4.5 decision tree classifier and Naive Bayes classification. The algorithms are experimentally evaluated on 50 data sets obtained from DMOZ (Open Directory Project). The experimental results indicate that artificial immune based systems achieve higher predictive performance for web page classification.
dc.identifier.issn2194-5357
dc.identifier.other2194-5365
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/2118
dc.language.isoEnglish
dc.publisherSPRINGER-VERLAG BERLIN
dc.subjectFEATURE-SELECTION
dc.titleArtificial Immune System Based Web Page Classification
dc.typeProceedings Paper

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