@article{(Open Science Index):https://publications.waset.org/pdf/10000701,
	  title     = {Feature Selection for Web Page Classification Using Swarm Optimization},
	  author    = {B. Leela Devi and  A. Sankar},
	  country	= {},
	  institution	= {},
	  abstract     = {The web’s increased popularity has included a huge
amount of information, due to which automated web page
classification systems are essential to improve search engines’
performance. Web pages have many features like HTML or XML
tags, hyperlinks, URLs and text contents which can be considered
during an automated classification process. It is known that Webpage
classification is enhanced by hyperlinks as it reflects Web page
linkages. The aim of this study is to reduce the number of features to
be used to improve the accuracy of the classification of web pages. In
this paper, a novel feature selection method using an improved
Particle Swarm Optimization (PSO) using principle of evolution is
proposed. The extracted features were tested on the WebKB dataset
using a parallel Neural Network to reduce the computational cost.
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {1},
	  year      = {2015},
	  pages     = {340 - 346},
	  ee        = {https://publications.waset.org/pdf/10000701},
	  url   	= {https://publications.waset.org/vol/97},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 97, 2015},