Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Lazy Learning of Bayesian Rules
Machine Learning
Machine Learning
Input Decimation Ensembles: Decorrelation through Dimensionality Reduction
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Oblivious decision trees graphs and top down pruning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We have previously introduced, in purely theoretical terms, the notion of neutral point substitution for missing kernel data in multimodal problems. In particular, it was demonstrated that when modalities are maximally disjoint, the method is precisely equivalent to the Sum rule decision scheme. As well as forging an intriguing analogy between multikernel and decision-combination methods, this finding means that the neutral-point method should exhibit a degree of resilience to class misattribution within the individual classifiers through the relative cancelling of combined estimation errors (if sufficiently decorrelated). However, the case of completely disjoint modalities is unrepresentative of the general missing data problem. We here set out to experimentally test the notion of neutral point substitution in a realistic experimental scenario with partially-disjoint data to establish the practical application of the method. The tested data consists in multimodal Biometric measurements of individuals in which the missing-modality problem is endemic. We hence test a SVM classifier under both the modal decision fusion and neutral point-substitution paradigms, and find that, while error cancellation is indeed apparent, the genuinely multimodal approach enabled by the neutral-point method is superior by a significant factor.