Integrated feature architecture selection

Authors:
J. M. Steppe;K. W. Bauer, Jr.;S. K. Rogers
Affiliations:
Dept. of Oper. Sci., Air Force Inst. of Technol., Wright-Patterson AFB, OH;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
1996

Citing 0
Cited 16

Selecting optimal experiments for multiple output multilayer perceptrons

Neural Computation
Feature selection with neural networks

Pattern Recognition Letters
Feature Extraction Based on ICA for Binary Classification Problems

IEEE Transactions on Knowledge and Data Engineering
Selecting salient features for classification based on neural network committees

Pattern Recognition Letters
On fuzzy-rough sets approach to feature selection

Pattern Recognition Letters
Measuring saliency of features extracted by model-based methods from internal carotid arterial Doppler signals using signal-to-noise ratios

Digital Signal Processing
Predictor output sensitivity and feature similarity-based feature selection

Fuzzy Sets and Systems
Modeling consumer situational choice of long distance communication with neural networks

Decision Support Systems
Measuring Saliency of Features Using Signal-to-noise Ratios for Detection of Electrocardiographic Changes in Partial Epileptic Patients

Journal of Medical Systems
Combined input variable selection and model complexity control for nonlinear regression

Pattern Recognition Letters
Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for Doppler ultrasound signals

Engineering Applications of Artificial Intelligence
Feature saliency using signal-to-noise ratios in automated diagnostic systems developed for ECG beats

Expert Systems with Applications: An International Journal
A predication survival model for colorectal cancer

AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
Genetic algorithms for feature selection of image analysis-based quality monitoring model: An application to an iron mine

Engineering Applications of Artificial Intelligence
Feature subset selection wrapper based on mutual information and rough sets

Expert Systems with Applications: An International Journal
Vision-based rock-type classification of limestone using multi-class support vector machine

Applied Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we present an integrated approach to feature and architecture selection for single hidden layer-feedforward neural networks trained via backpropagation. In our approach, we adopt a statistical model building perspective in which we analyze neural networks within a nonlinear regression framework. The algorithm presented in this paper employs a likelihood-ratio test statistic as a model selection criterion. This criterion is used in a sequential procedure aimed at selecting the best neural network given an initial architecture as determined by heuristic rules. Application results for an object recognition problem demonstrate the selection algorithm's effectiveness in identifying reduced neural networks with equivalent prediction accuracy