Rough set based ensemble classifier for web page classification

Saha, Suman ; Murthy, C. A. ; Pal, Sankar K. (2007) Rough set based ensemble classifier for web page classification Fundamenta Informaticae, 76 (1-2). pp. 171-187. ISSN 0169-2968

[img]
Preview
PDF - Publisher Version
134kB

Official URL: http://iospress.metapress.com/content/ae5krl9bkxxn...

Abstract

Combining the results of a number of individually trained classification systems to obtain a more accurate classifier is a widely used technique in pattern recognition. In this article, we have introduced a rough set based meta classifier to classify web pages. The proposed method consists of two parts. In the first part, the output of every individual classifier is considered for constructing a decision table. In the second part, rough set attribute reduction and rule generation processes are used on the decision table to construct a meta classifier. It has been shown that (1) the performance of the meta classifier is better than the performance of every constituent classifier and, (2) the meta classifier is optimal with respect to a quality measure defined in the article. Experimental studies show that the meta classifier improves accuracy of classification uniformly over some benchmark corpora and beats other ensemble approaches in accuracy by a decisive margin, thus demonstrating the theoretical results. Apart from this, it reduces the CPU load compared to other ensemble classification techniques by removing redundant classifiers from the combination.

Item Type:Article
Source:Copyright of this article belongs to IOS Press.
Keywords:Text Classification; Rough Set; Meta Classifier
ID Code:26106
Deposited On:06 Dec 2010 13:05
Last Modified:17 May 2016 09:27

Repository Staff Only: item control page