Machine Learning for Web Page Classification: A Survey
Abstract
The Internet contains a vast amount of data that is growing exponentially. To exploit this data, a Web information retrieval system and a categorization of internet content based on the classification of web pages are essential. Web page classification has many applications, among them the construction of web directories and the building of focused crawlers. In this paper, we present the characteristics of web page classification, we produce a literature review by summarizing and evaluating all sources related to web page classification crawled automatically from ScienceDirect and Springer websites, we review the different machine learning algorithms used to categorize web pages. Finally, we track the underlying assumptions behind the studied methods.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
In order for iJIST to publish and disseminate research articles, we need publishing rights. This is determined by a publishing agreement between the author and iJIST.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.
Privacy Statement
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.
