Neural Information Processing
Feature Ranking Ensembles for Facial Action Unit Classification
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Involving New Local Search in Hybrid Genetic Algorithm for Feature Selection
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Stopping criteria for ensemble-based feature selection
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Load identification of non-intrusive load-monitoring system in smart home
WSEAS TRANSACTIONS on SYSTEMS
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
A minority class feature selection method
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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This paper presents a novel feature selection approach for backpropagation neural networks (NNs). Previously, a feature selection technique known as the wrapper model was shown effective for decision trees induction. However, it is prohibitively expensive when applied to real-world neural net training characterized by large volumes of data and many feature choices. Our approach incorporates a weight analysis-based heuristic called artificial neural net input gain measurement approximation (ANNIGMA) to direct the search in the wrapper model and allows effective feature selection feasible for neural net applications. Experimental results on standard datasets show that this approach can efficiently reduce the number of features while maintaining or even improving the accuracy. We also report two successful applications of our approach in the helicopter maintenance applications