Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application
| Autor: | Rohit Rampal, Vivian West, Paul Mangiameli, David West |
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| Rok vydání: | 2005 |
| Předmět: |
Decision support system
education.field_of_study Information Systems and Management General Computer Science Artificial neural network Computer science Process (engineering) business.industry Model selection Population Supervised learning Management Science and Operations Research Machine learning computer.software_genre Generalization error Industrial and Manufacturing Engineering Modeling and Simulation Independence (mathematical logic) Artificial intelligence education business computer Selection (genetic algorithm) |
| Zdroj: | European Journal of Operational Research. 162:532-551 |
| ISSN: | 0377-2217 |
| DOI: | 10.1016/j.ejor.2003.10.013 |
| Popis: | The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system based on supervised learning algorithms. This research investigates the potential of various selection strategies from a population of 24 classification models to form ensembles in order to increase the accuracy of decision support systems for the early detection and diagnosis of breast cancer. Our results suggest that ensembles formed from a diverse collection of models are generally more accurate than either pure-bagging ensembles (formed from a single model) or the selection of a “single best model.” We find that effective ensembles are formed from a small and selective subset of the population of available models with potential candidates identified by a multicriteria process that considers the properties of model generalization error, model instability, and the independence of model decisions relative to other ensemble members. |
| Databáze: | OpenAIRE |
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