Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Comparative Study of Feature-Salience Ranking Techniques
Neural Computation
A Theoretical Basis for the Analysis of Multiversion Software Subject to Coincident Errors
IEEE Transactions on Software Engineering
Engineering multiversion neural-net systems
Neural Computation
Monte Carlo theory as an explanation of bagging and boosting
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Intelligent Decision Support System for Osteoporosis Prediction
International Journal of Intelligent Information Technologies
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This paper presents the research in developing an ensemble of data mining techniques for predicting the risk of osteoporosis prevalence in women. Osteoporosis is a bone disease that commonly occurs among postmenopausal women. Early detection and diagnosis is the key for prevention but are very difficult, without using costly diagnosing devices, due to complex factors involved and its gradual bone lose process with no obvious waning symptoms in particular. Our research aims to develop an intelligent decision support system based on data mining ensemble technology to assist General Practitioners in assessing patient's risk of developing osteoporosis. The paper focuses on investigating the methodologies for constructing effective ensembles, specifically on the measurements of diversity between individual models induced by two types of machine learning techniques, i.e. neural networks and decision tress for predicting the risk of osteoporosis. The constructed ensembles as well as their member predictors are assessed in terms of reliability, diversity and accuracy of prediction. The results indicate that the intelligently hybridised ensembles have high-level diversities and thus are able to improve their performance.