Abstract
In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2003 ACM Symposium on Document Engineering |
| Editors | C. Vanoirbeek, C. Roisin, E. Munson |
| Pages | 118-120 |
| Number of pages | 3 |
| Publication status | Published - Dec 1 2003 |
| Event | Proceedings of the 2003 ACM Symposium on Document Engineering - Grenoble, France Duration: Nov 20 2003 → Nov 22 2003 |
Publication series
| Name | Proceedings of the 2003 ACM Symposium on Document Engineering |
|---|
Other
| Other | Proceedings of the 2003 ACM Symposium on Document Engineering |
|---|---|
| Country | France |
| City | Grenoble |
| Period | 11/20/03 → 11/22/03 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
Cite this
Accuracy improvement of automatic text classification based on feature transformation. / Zu, Guowei; Ohyama, Wataru; Wakabayashi, Tetsushi; Kimura, Fumitaka.
Proceedings of the 2003 ACM Symposium on Document Engineering. ed. / C. Vanoirbeek; C. Roisin; E. Munson. 2003. p. 118-120 (Proceedings of the 2003 ACM Symposium on Document Engineering).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Accuracy improvement of automatic text classification based on feature transformation
AU - Zu, Guowei
AU - Ohyama, Wataru
AU - Wakabayashi, Tetsushi
AU - Kimura, Fumitaka
PY - 2003/12/1
Y1 - 2003/12/1
N2 - In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM.
AB - In this paper, we describe a comparative study on techniques of feature transformation and classification to improve the accuracy of automatic text classification. The normalization to the relative word frequency, the principal component analysis (K-L transformation) and the power transformation were applied to the feature vectors, which were classified by the Euclidean distance, the linear discriminant function, the projection distance, the modified projection distance and the SVM.
UR - http://www.scopus.com/inward/record.url?scp=3543055806&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=3543055806&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:3543055806
SN - 1581137249
SN - 9781581137248
T3 - Proceedings of the 2003 ACM Symposium on Document Engineering
SP - 118
EP - 120
BT - Proceedings of the 2003 ACM Symposium on Document Engineering
A2 - Vanoirbeek, C.
A2 - Roisin, C.
A2 - Munson, E.
ER -