Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Explaining Predictions from a Neural Network Ensemble One at a Time
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Model selection for medical diagnosis decision support systems
Decision Support Systems
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Artificial Intelligence in Medicine
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Diversity of ability and cognitive style for group decision processes
Information Sciences: an International Journal
Selective ensemble of decision trees
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Municipal revenue prediction by ensembles of neural networks and support vector machines
WSEAS Transactions on Computers
Evolution of cartesian genetic programs for development of learning neural architecture
Evolutionary Computation
Explaining the output of ensembles in medical decision support on a case by case basis
Artificial Intelligence in Medicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
The problem of bias in training data in regression problems in medical decision support
Artificial Intelligence in Medicine
The build of n-Bits Binary Coding ICBP Ensemble System
Neurocomputing
Advances in Fuzzy Systems - Special issue on Hybrid Biomedical Intelligent Systems
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
| Hi-index | 0.00 |
Artificial neural networks (ANNs) are very popular as classification or regression mechanisms in medical decision support systems despite the fact that they are unstable predictors. This instability means that small changes in the training data used to build the model (i.e. train the ANN) may result in very different models. A central implication of this is that different sets of training data may produce models with very different generalisation accuracies. In this paper, we show in detail how this can happen in a prediction system for use in in-vitro fertilisation. We argue that claims for the generalisation performance of ANNs used in such a scenario should only be based on k-fold cross-validation tests. We also show how the accuracy of such a predictor can be improved by aggregating the output of several predictors.