Selecting variables for neural network committees

Authors:
Marija Bacauskiene;Vladas Cibulskis;Antanas Verikas
Affiliations:
Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania;Department of Applied Electronics, Kaunas University of Technology, Kaunas, Lithuania
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 8
Cited 2

Empirical methods for artificial intelligence

Empirical methods for artificial intelligence
Soft combination of neural classifiers: a comparative study

Pattern Recognition Letters
Ensemble learning via negative correlation

Neural Networks
Feature selection with neural networks

Pattern Recognition Letters
Local overfitting control via leverages

Neural Computation
Selecting Neural Networks for Making a Committee Decision

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Selecting salient features for classification based on neural network committees

Pattern Recognition Letters
Neural-network feature selector

IEEE Transactions on Neural Networks

Evolving Committees of Support Vector Machines

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
A feature selection technique for generation of classification committees and its application to categorization of laryngeal images

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of the variable selection is threefold: to reduce model complexity, to promote diversity of committee networks, and to find a trade-off between the accuracy and diversity of the networks. To achieve the goal, the steps of neural network training, aggregation, and elimination of irrelevant input variables are integrated based on the negative correlation learning [1] error function. Experimental tests performed on three real world problems have shown that statistically significant improvements in classification performance can be achieved from neural network committees trained according to the technique proposed.