Cart (Loading....) | Create Account
Close category search window
 

A New Centroid-Based Classifier for Text Categorization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Lifei Chen ; Xiamen Univ., Xiamen ; Yanfang Ye ; Qingshan Jiang

In recent years, centroid-based document classifiers receive wide interests from text mining community because of their simplicity and linear-time complexity. However, the traditional centroid-based classifiers usually perform less effectively for Chinese text categorization. In this paper, we tackle the problem by developing a new way to calculate the class-specific weights for each term in the training phase; in the testing phase, the new documents are assigned to the centroid to which the document is most similar based on the weighted distance measurement. The experimental results demonstrate that the accuracy of our algorithm outperforms the traditional centroid-based classifiers, as well as outstanding efficiency compared with the Support Vector Machine (SVM) based classifiers for Chinese text categorization.

Published in:

Advanced Information Networking and Applications - Workshops, 2008. AINAW 2008. 22nd International Conference on

Date of Conference:

25-28 March 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.