Multilayer feedforward networks are universal approximators
Neural Networks
Connectionist learning procedures
Artificial Intelligence
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Machine Learning
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Ensembling neural networks: many could be better than all
Artificial Intelligence
Linear Decision Fusions in Multilayer Perceptrons for Breast Cancer Diagnosis
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Unbiased Linear Neural-Based Fusion with Normalized Weighted Average Algorithm for Regression
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
An automatic diagnosis method for the knee meniscus tears in MR images
Expert Systems with Applications: An International Journal
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Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.