Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Instance-Based Learning Algorithms
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
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Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Exact Probability Metric for Decision Tree Splitting and Stopping
Machine Learning
Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Machine Learning - Special issue on learning with probabilistic representations
Representing the behaviour of supervised classification learning algorithms by Bayesian networks
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Expert Systems and Probabiistic Network Models
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Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Machine Learning
Machine Learning
Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit
AIMDM '99 Proceedings of the Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making
A study of distance-based machine learning algorithms
A study of distance-based machine learning algorithms
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Mining data from intensive care patients
Advanced Engineering Informatics
Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome
Expert Systems with Applications: An International Journal
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
On a unified framework for sampling with and without replacement in decision tree ensembles
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Image analysis and automatic surface identification by a bi-level multi-classifier
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
Bayesian network multi-classifiers for protein secondary structure prediction
Artificial Intelligence in Medicine
Formal-Transfer In and Out of Stroke Care Units: An Analysis Using Bayesian Networks
International Journal of Healthcare Information Systems and Informatics
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
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Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.