JOURNAL ARTICLE

Neural Network Ensemble Based on Feature Selection

Abstract

In this paper, a novel neural network ensemble model, i.e. NNEIPCABag, which combines the feature selection technique, the improved principal component analysis (IPCA), with the Bagging method, is presented. Then the proposed model is employed for time series forecasting with the favor results obtained, which shows that the generalization ability of the proposed model can be superior to that of neural network ensemble only with the Bagging method, .i.e. NNEBag.

Keywords:
Artificial neural network Computer science Generalization Artificial intelligence Feature selection Principal component analysis Feature (linguistics) Selection (genetic algorithm) Pattern recognition (psychology) Ensemble forecasting Machine learning Ensemble learning Time series Series (stratigraphy) Mathematics

Metrics

3
Cited By
0.00
FWCI (Field Weighted Citation Impact)
7
Refs
0.10
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Neural Networks and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Algorithms and Applications
Physical Sciences →  Engineering →  Control and Systems Engineering
Advanced Computational Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence

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