Abstract
There exist redundant, irrelevant and noisy data. Using proper data to train a network can speed up training, simplify the learned structure, and improve its performance. A two-phase training algorithm is proposed. In the first phase, the number of input units of the network is determined by using an information base method. Only those attributes that meet certain criteria for inclusion will be considered as the input to the network. In the second phase, the number of hidden units of the network is selected automatically based on the performance of the network on the training data. One hidden unit is added at a time only if it is necessary. The experimental results show that this new algorithm can achieve a faster learning time, a simpler network and an improved performance.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 129-139 |
| Number of pages | 11 |
| Journal | Applied Intelligence |
| Volume | 6 |
| Issue number | 2 |
| State | Published - 1996 |
| Externally published | Yes |
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Keywords
- Backpropagation
- Feature selection
- Feedforward neural network
- Information theory
ASJC Scopus subject areas
- Artificial Intelligence
- Control and Systems Engineering
Cite this
Improving backpropagation learning with feature selection. / Setiono, Rudy; Liu, Huan.
In: Applied Intelligence, Vol. 6, No. 2, 1996, p. 129-139.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Improving backpropagation learning with feature selection
AU - Setiono, Rudy
AU - Liu, Huan
PY - 1996
Y1 - 1996
N2 - There exist redundant, irrelevant and noisy data. Using proper data to train a network can speed up training, simplify the learned structure, and improve its performance. A two-phase training algorithm is proposed. In the first phase, the number of input units of the network is determined by using an information base method. Only those attributes that meet certain criteria for inclusion will be considered as the input to the network. In the second phase, the number of hidden units of the network is selected automatically based on the performance of the network on the training data. One hidden unit is added at a time only if it is necessary. The experimental results show that this new algorithm can achieve a faster learning time, a simpler network and an improved performance.
AB - There exist redundant, irrelevant and noisy data. Using proper data to train a network can speed up training, simplify the learned structure, and improve its performance. A two-phase training algorithm is proposed. In the first phase, the number of input units of the network is determined by using an information base method. Only those attributes that meet certain criteria for inclusion will be considered as the input to the network. In the second phase, the number of hidden units of the network is selected automatically based on the performance of the network on the training data. One hidden unit is added at a time only if it is necessary. The experimental results show that this new algorithm can achieve a faster learning time, a simpler network and an improved performance.
KW - Backpropagation
KW - Feature selection
KW - Feedforward neural network
KW - Information theory
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UR - http://www.scopus.com/inward/citedby.url?scp=0030129019&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0030129019
VL - 6
SP - 129
EP - 139
JO - Applied Intelligence
JF - Applied Intelligence
SN - 0924-669X
IS - 2
ER -