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 language | English |
|---|---|
| Title of host publication | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
| Pages | 1519-1523 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2008 Dec 1 |
| Event | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 - Singapore, Singapore Duration: 2008 Dec 8 → 2008 Dec 11 |
Publication series
| Name | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
|---|
Other
| Other | 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 08-12-08 → 08-12-11 |
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All Science Journal Classification (ASJC) codes
- Management Information Systems
- Industrial and Manufacturing Engineering
Cite this
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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 proceeding › Conference contribution
TY - GEN
T1 - One-against-one fuzzy support vector machine text categorization classifier
AU - Chiang, H. M.
AU - Wang, Tai-Yue
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=62749109394&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62749109394&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2008.4738125
DO - 10.1109/IEEM.2008.4738125
M3 - Conference contribution
SN - 9781424426300
T3 - 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
SP - 1519
EP - 1523
BT - 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
ER -