Abstract
Incorporating a method of feature selection into a classification model often provides a number of advantages. In this paper we propose a new feature selection method based on the discriminative perspective of improving the classification accuracy. The feature selection method is developed for a classification model for text chunking. For effective feature selection, we utilize a decision tree as an intermediate feature space inducer. To select a more compact feature set with less computational load, we organized a partially ordered feature space according to the IGR distribution of features. Experimental results show that: (1) the computational complexity on high-dimensional feature space can be reduced by selecting features based on the decision tree decomposition; (2) the text chunking system using the proposed feature selection can significantly improve the performance compared with a decision tree classifier.
| Original language | English |
|---|---|
| Title of host publication | ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2217-2222 |
| Number of pages | 6 |
| Volume | 5 |
| ISBN (Electronic) | 9810475241, 9789810475246 |
| DOIs | |
| Publication status | Published - 2002 |
| Event | 9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore Duration: 2002 Nov 18 → 2002 Nov 22 |
Other
| Other | 9th International Conference on Neural Information Processing, ICONIP 2002 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 02/11/18 → 02/11/22 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
Decision tree decomposition-based complex feature selection for text chunking. / Hwang, Young Sook; Rim, Hae-Chang.
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Vol. 5 Institute of Electrical and Electronics Engineers Inc., 2002. p. 2217-2222 1201887.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Decision tree decomposition-based complex feature selection for text chunking
AU - Hwang, Young Sook
AU - Rim, Hae-Chang
PY - 2002
Y1 - 2002
N2 - Incorporating a method of feature selection into a classification model often provides a number of advantages. In this paper we propose a new feature selection method based on the discriminative perspective of improving the classification accuracy. The feature selection method is developed for a classification model for text chunking. For effective feature selection, we utilize a decision tree as an intermediate feature space inducer. To select a more compact feature set with less computational load, we organized a partially ordered feature space according to the IGR distribution of features. Experimental results show that: (1) the computational complexity on high-dimensional feature space can be reduced by selecting features based on the decision tree decomposition; (2) the text chunking system using the proposed feature selection can significantly improve the performance compared with a decision tree classifier.
AB - Incorporating a method of feature selection into a classification model often provides a number of advantages. In this paper we propose a new feature selection method based on the discriminative perspective of improving the classification accuracy. The feature selection method is developed for a classification model for text chunking. For effective feature selection, we utilize a decision tree as an intermediate feature space inducer. To select a more compact feature set with less computational load, we organized a partially ordered feature space according to the IGR distribution of features. Experimental results show that: (1) the computational complexity on high-dimensional feature space can be reduced by selecting features based on the decision tree decomposition; (2) the text chunking system using the proposed feature selection can significantly improve the performance compared with a decision tree classifier.
UR - http://www.scopus.com/inward/record.url?scp=84971673630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84971673630&partnerID=8YFLogxK
U2 - 10.1109/ICONIP.2002.1201887
DO - 10.1109/ICONIP.2002.1201887
M3 - Conference contribution
AN - SCOPUS:84971673630
VL - 5
SP - 2217
EP - 2222
BT - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PB - Institute of Electrical and Electronics Engineers Inc.
ER -