Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the Practice of Classifier Performance Assessment
Neural Computation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Adjusted estimation for the combination of classifiers
Intelligent Data Analysis
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Multiple classifier systems fall into two types: classifier combination systems and classifier choice systems. The former aggregate component systems to produce an overall classification, while the latter choose between component systems to decide which classification rule to use. We illustrate each type applied in a real context where practical constraints limit the type of base classifier which can be used. In particular, our context - that of credit scoring - favours the use of simple interpretable, especially linear, forms. Simple measures of classification performance are just one way of measuring the suitability of classification rules in this context.