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 |
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| Title of host publication | Proceedings of the 2003 ACM Symposium on Document Engineering |
| Publisher | Association for Computing Machinery |
| Pages | 118-120 |
| Number of pages | 3 |
| ISBN (Print) | 1581137249, 9781581137248 |
| DOIs | |
| Publication status | Published - 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 |
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Other
| Other | Proceedings of the 2003 ACM Symposium on Document Engineering |
|---|---|
| Country | France |
| City | Grenoble |
| Period | 11/20/03 → 11/22/03 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
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Cite this
Zu, G., Ohyama, W., Wakabayashi, T., & Kimura, F. (2003). Accuracy improvement of automatic text classification based on feature transformation. In Proceedings of the 2003 ACM Symposium on Document Engineering (pp. 118-120). (Proceedings of the 2003 ACM Symposium on Document Engineering). Association for Computing Machinery. https://doi.org/10.1145/958238.958242