Web page classification based on a simplified swarm optimization

Ji-Hyun Lee, Wei-Chang Yeh and Mei-Chi Chuang

Applied Mathematics and Computation, 2015, vol. 270, issue C, 13-24

Abstract: Owing to the incredible increase in the amount of information on the World Wide Web, there is a strong need for an efficient web page classification to retrieve useful information quickly. In this paper, we propose a novel simplified swarm optimization (SSO) to learn the best weights for every feature in the training dataset and adopt the best weights to classify the new web pages in the testing dataset. Moreover, the parameter settings play an important role in the update mechanism of the SSO so that we utilize a Taguchi method to determine the parameter settings. In order to demonstrate the effectiveness of the algorithm, we compare its performance with that of the well-known genetic algorithm (GA), Bayesian classifier, and K-nearest neighbor (KNN) classifiers according to four datasets. The experimental results indicate that the SSO yields better performance than the other three approaches.

Keywords: Web page classification; Simplified swarm optimization; Taguchi method (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300315010425
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:270:y:2015:i:c:p:13-24

DOI: 10.1016/j.amc.2015.07.120

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().