
	Journal of Applied Sciences
	Year: 2010 \| Volume: 10 \| Issue: 13 \| Page No.: 1243-1254 DOI: 10.3923/jas.2010.1243.1254

Tournament Structure Ranking Techniques for Bayesian Text Classification with Highly Similar Categories

L.H. Lee, D. Isa, W.O. Choo and W.Y. Chue

Abstract: This study implements a series of tournament structure ranking technique to improve the classification accuracy of conventional Bayesian classification, especially in handling classification tasks with highly similar categories. Bayesian classification approach has been widely implemented in many real-world text categorization applications due to its simplicity, low cost training and classifying algorithms and ability in handling raw text data directly without needing extensive pre-processes. However, Bayesian classification has been reported as one of the poor-performing classification approaches. The poor performance of the Bayesian classification is critical especially in handling text classification tasks with multiple highly similar categories. In this study, we introduce a series of tournament structure based ranking classification techniques to overcome the low accuracy of conventional Bayesian classification which implements the flat ranking technique. Experiments that have been conducted in this research to show that the proposed Bayesian classifier embedded with tournament structure ranking techniques is able to ensure promising performance while dealing with knowledge domains with highly similar categories. This is due to the enhanced Bayesian classifier performs its classification tasks based on the implementation of multiple, iterative and isolated binary classifications and thus guarantee a low-error-rate Bayesian classification. As the result, an enhanced Bayesian classifier which is applicable to different types of domains of varying characteristics is introduced to handle the real world text classification problems effectively and efficiently.

PDF Fulltext XML References Citation Report Citation

RELATED ARTICLES:

•	Classifier Design Algorithms Aimed at Overlapping Characteristics

•	A Review of Nearest Neighbor-Support Vector Machines Hybrid Classification Models

•	Three Phase Induction Motor Faults Detection by Using Radial Basis Function Neural Network

•	Power System On-Line Static Security Assessment by Using Multi-Class Support Vector Machines

•	Feature Extraction and Classification of Objects in the Rosette Pattern Using Component Analysis and Neural Network

How to cite this article:

L.H. Lee, D. Isa, W.O. Choo and W.Y. Chue, 2010. Tournament Structure Ranking Techniques for Bayesian Text Classification with Highly Similar Categories. Journal of Applied Sciences, 10: 1243-1254.

DOI: 10.3923/jas.2010.1243.1254

URL: https://scialert.net/abstract/?doi=jas.2010.1243.1254

COMMENT ON THIS PAPER

Navigation


		Online First
		Current Issue
		Previous Issues
		Editorial Board
		Submit a Manuscript
		Guide to Authors
		Subscribe to E-alerts

Indexed In


		AGRIS
		ASCI-Database
		Asian Digital Library
		Google Scholar

Science Alert