Abstract
In this paper, a new feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected employing the recently published decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, we attempt to reduce the training time of the neural network and obtain a simpler neural network, further reducing the classification time for test data. Experiments show promising results.
| Original language | English |
|---|---|
| Title of host publication | IGARSS 1992 - International Geoscience and Remote Sensing Symposium |
| Subtitle of host publication | International Space Year: Space Remote Sensing |
| Editors | Ruby Williamson, Tammy Stein |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 839-841 |
| Number of pages | 3 |
| ISBN (Electronic) | 0780301382 |
| DOIs | |
| Publication status | Published - 1992 Jan 1 |
| Event | 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 - Houston, United States Duration: 1992 May 26 → 1992 May 29 |
Publication series
| Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
|---|---|
| Volume | 2 |
Other
| Other | 12th Annual International Geoscience and Remote Sensing Symposium, IGARSS 1992 |
|---|---|
| Country | United States |
| City | Houston |
| Period | 92/5/26 → 92/5/29 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Earth and Planetary Sciences(all)
Cite this
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Feature selection for neural networks using Parzen density estimator. / Lee, Chulhee; Benediktsson, Jon A.; Landgrebe, David A.
IGARSS 1992 - International Geoscience and Remote Sensing Symposium: International Space Year: Space Remote Sensing. ed. / Ruby Williamson; Tammy Stein. Institute of Electrical and Electronics Engineers Inc., 1992. p. 839-841 578271 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Feature selection for neural networks using Parzen density estimator
AU - Lee, Chulhee
AU - Benediktsson, Jon A.
AU - Landgrebe, David A.
PY - 1992/1/1
Y1 - 1992/1/1
N2 - In this paper, a new feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected employing the recently published decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, we attempt to reduce the training time of the neural network and obtain a simpler neural network, further reducing the classification time for test data. Experiments show promising results.
AB - In this paper, a new feature selection method for neural networks is proposed using the Parzen density estimator. A new feature set is selected employing the recently published decision boundary feature selection algorithm. The selected feature set is then used to train a neural network. Using a reduced feature set, we attempt to reduce the training time of the neural network and obtain a simpler neural network, further reducing the classification time for test data. Experiments show promising results.
UR - http://www.scopus.com/inward/record.url?scp=84964523892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964523892&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.1992.578271
DO - 10.1109/IGARSS.1992.578271
M3 - Conference contribution
AN - SCOPUS:84964523892
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 839
EP - 841
BT - IGARSS 1992 - International Geoscience and Remote Sensing Symposium
A2 - Williamson, Ruby
A2 - Stein, Tammy
PB - Institute of Electrical and Electronics Engineers Inc.
ER -