An Embedded-Based Weighted Feature Selection Algorithm for Classifying Web Document. (15th September 2020)

• Record Type: Journal Article
• Authors: Siva Shankar, G.; Ashokkumar, P.; Vinayakumar, R.; Ghosh, Uttam; Mansoor, Wathiq; Alnumay, Waleed S.
• Other Names: Wang Xiaojie Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-09-15
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/8879054
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : With the exponential increase in a number of web pages daily, it makes it very difficult for a search engine to list relevant web pages. In this paper, we propose a machine learning-based classification model that can learn the best features in each web page and helps in search engine listing. The existing methods for listing have lots of drawbacks like interfacing the normal operations of the website and crawling lots of useless information. Our proposed algorithm provides an optimal classification for websites which has a large number of web pages such as Wikipedia by just considering core information like link text, side information, and header text. We implemented our algorithm with standard benchmark datasets, and the results show that our algorithm outperforms the existing algorithms.