IEICE TRANSACTIONS on Information
Improving Automatic Text Classification by Integrated Feature Analysis
Summary :
Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.
- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.4 pp.1101-1109
- Publication Date
- 2008/04/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e91-d.4.1101
- Type of Manuscript
- PAPER
- Category
- Pattern Recognition
Authors
Keyword
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Lazaro S.P. BUSAGALA, Wataru OHYAMA, Tetsushi WAKABAYASHI, Fumitaka KIMURA, "Improving Automatic Text Classification by Integrated Feature Analysis" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 4, pp. 1101-1109, April 2008, doi: 10.1093/ietisy/e91-d.4.1101.
Abstract: Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.4.1101/_p
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@ARTICLE{e91-d_4_1101,
author={Lazaro S.P. BUSAGALA, Wataru OHYAMA, Tetsushi WAKABAYASHI, Fumitaka KIMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Improving Automatic Text Classification by Integrated Feature Analysis},
year={2008},
volume={E91-D},
number={4},
pages={1101-1109},
abstract={Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.},
keywords={},
doi={10.1093/ietisy/e91-d.4.1101},
ISSN={1745-1361},
month={April},}
Copy
TY - JOUR
TI - Improving Automatic Text Classification by Integrated Feature Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 1101
EP - 1109
AU - Lazaro S.P. BUSAGALA
AU - Wataru OHYAMA
AU - Tetsushi WAKABAYASHI
AU - Fumitaka KIMURA
PY - 2008
DO - 10.1093/ietisy/e91-d.4.1101
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2008
AB - Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimensionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimensionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness.
ER -
English
