Abstract
In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.
| Original language | English |
|---|---|
| Pages (from-to) | 75-83 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Neural Networks |
| Volume | 8 |
| Issue number | 1 |
| Publication status | Published - 1997 Dec 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Computer Science Applications
- Computer Networks and Communications
- Artificial Intelligence
Cite this
}
Decision boundary feature extraction for neural networks. / Lee, Chul Hee; Landgrebe, David A.
In: IEEE Transactions on Neural Networks, Vol. 8, No. 1, 01.12.1997, p. 75-83.Research output: Contribution to journal › Article
TY - JOUR
T1 - Decision boundary feature extraction for neural networks
AU - Lee, Chul Hee
AU - Landgrebe, David A.
PY - 1997/12/1
Y1 - 1997/12/1
N2 - In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.
AB - In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results.
UR - http://www.scopus.com/inward/record.url?scp=0030855587&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030855587&partnerID=8YFLogxK
M3 - Article
C2 - 18255612
AN - SCOPUS:0030855587
VL - 8
SP - 75
EP - 83
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
SN - 2162-237X
IS - 1
ER -