On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
The Bias Variance Trade-Off in Bootstrapped Error Correcting Output Code Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
On Feature Selection, Bias-Variance, and Bagging
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Combining bias and variance reduction techniques for regression trees
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Classifier decision fusion has been shown to act in a manner analogous to the back-projection of Radon transformations when individual classifier feature sets are non or partially overlapping. It is possible, via this analogy, to demonstrate that standard linear classifier fusion introduces a morphological bias into the decision space due to the implicit angular undersampling of the feature selection process. In standard image-based (eg medical) tomography, removal of this bias involves a filtration process, and an analogous n-dimensional processes can be shown to exist for decision fusion using Högbom deconvolution. Countering the biasing process implicit in linear fusion, however, is the fact that back projection of Radon transformation (being additive) should act to reduce variance within the composite decision space. In principle, this additive variance-reduction should still apply to tomographically- filtered back-projection, unless the filtration process contravenes. We therefore argue that when feature selection is carried-out independently for each classifier (as in e.g. multi-modal problems) unfiltered decision fusion, while in general being variance-decreasing, is typically also bias-increasing. By employing a shot noise model, we seek to quantify how far filtration acts to rectify this problem, such that feature selection can be made both bias and variance reducing within an ensemble fusion context.