One-against-one fuzzy support vector machine text categorization classifier

H. M. Chiang, Tai-Yue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The growth of the internet information delivery has made automatic text categorization essential. This investigation explores the challenges of multi-class text categorization using one-against-one fuzzy support vector machine with Reuter's news as the example data. While the fuzzy set theory is incorporated into the OAO-SVM in the classifying module, the influence of the samples with high uncertainty can be decreased as the fuzzy membership functions are to used to weigh the margin of each training vector. The performances of four different membership functions on one-against-one fuzzy support vector machine are measured using the macroaverage performance indices. Analytical results indicate that the proposed method achieves a comparable or better performance than the one-against-one support vector machine.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
Pages1519-1523
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 - Singapore, Singapore
Duration: 2008 Dec 82008 Dec 11

Publication series

Name2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008

Other

Other2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
CountrySingapore
CitySingapore
Period08-12-0808-12-11

Fingerprint

Support vector machines
Classifiers
Membership functions
Fuzzy set theory
Internet
Text categorization
Support vector machine
Classifier
Membership function
Uncertainty
News
Performance index
Margin
Module
World Wide Web

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Industrial and Manufacturing Engineering

Cite this

Chiang, H. M., & Wang, T-Y. (2008). One-against-one fuzzy support vector machine text categorization classifier. In 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 (pp. 1519-1523). [4738125] (2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008). https://doi.org/10.1109/IEEM.2008.4738125
Chiang, H. M. ; Wang, Tai-Yue. / One-against-one fuzzy support vector machine text categorization classifier. 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008. 2008. pp. 1519-1523 (2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008).
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Chiang, HM & Wang, T-Y 2008, One-against-one fuzzy support vector machine text categorization classifier. in 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008., 4738125, 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, pp. 1519-1523, 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, Singapore, Singapore, 08-12-08. https://doi.org/10.1109/IEEM.2008.4738125

One-against-one fuzzy support vector machine text categorization classifier. / Chiang, H. M.; Wang, Tai-Yue.

2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008. 2008. p. 1519-1523 4738125 (2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chiang HM, Wang T-Y. One-against-one fuzzy support vector machine text categorization classifier. In 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008. 2008. p. 1519-1523. 4738125. (2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008). https://doi.org/10.1109/IEEM.2008.4738125