Methods for combining experts' probability assessments
Neural Computation
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new hybrid approach in combining multiple experts to recognise handwritten numerals
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combined Classifier Optimisation via Feature Selection
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Classifier Combination as a Tomographic Process
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Boosting and other ensemble methods
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
The practical performance characteristics of tomographically filtered multiple classifier fusion
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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We have previously (MCS2001) presented a mathematical metaphor setting out an equivalence between multiple expert fusion and the process of tomographic reconstruction familiar from medical imaging. However, the discussion took place only in relation to a restricted case: namely, classifiers containing discrete feature sets. This, its sequel paper, will therefore endeavour to extend the methodology to the fully general case.The investigation is thus conducted initially within the context of classical feature selection (that is, selection algorithms that place no restriction upon the overlap of feature sets), the findings in relation to which demonstrating the necessity of a re-evaluation of the role of feature-selection when conducted within an explicitly combinatorial framework. When fully enunciated, the resulting investigation leads naturally to a completely generalised, morphologically-optimal strategy for classifier combination.