Feature selection with neural networks

Pattern Recognition Letters

Volumn 23, Issue 11, 2002, Pages 1323-1335

  Verikas, A ^a,b   Bacauskiene, M ^b  

^a HALMSTAD UNIVERSITY   (Sweden)

^b KAUNAS UNIVERSITY OF TECHNOLOGY   (Lithuania)

Author keywords

Classification; Feature selection; Neural network; Regularization

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); ERRORS; NEURAL NETWORKS; TRANSFER FUNCTIONS;

FEATURE SELECTION;

FEATURE EXTRACTION;

ARTIFICIAL NEURAL NETWORK;

EID: 0036721934     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(02)00081-8     Document Type: Article

References (28)

1. Feature selection using radial basis function networks
2. Feature screening using signal-to-noise ratios
3. Determining input features for multilayer perceptions
4. Variable selection with neural networks
5. Feature analysis: Neural network and fuzzy set theoretic approaches
6. Feature selection for automatic classification of non-Gaussian data
7. Feature selection: Evaluation, application, and small sample performance
8. Optimum feature selection by zero-one integerprogramming
9. Merging back-propagation and Hebian learning rules for robust classifications
10. Feature selection and extraction
11. Bayes-optimality motivated-linear and multilayered perceptron-based dimansionality reduction
12. A comparison of seven techniques for choosing subsets of pattern recognition properties
13. A branch and bound algorithm for feature selection
14. Soft computing for feature analysis
15. Unsupervised feature evaluation: A neuro-fuzzy approach
16. Image enhancement and thresholding by optimization of fuzzy compactness
17. Bayesian selection of important features for feedforward neural networks
18. Floating search methods in feature selection
19. Pruning algorithms - A survey
20. Neural-network feature selector
21. Improved feature screening in feedforward neural networks
22. Integrated feature and architecture selection
23. Pertubation method for deleting redundant inputs of perception networks