The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unlabeled Data Can Degrade Classification Performance of Generative Classifiers
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Adaptive Learning Rate for Online Linear Discriminant Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Using Real-Time fMRI to Control a Dynamical System by Brain Activity Classification
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
On-Line fMRI Data Classification Using Linear and Ensemble Classifiers
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
A Brain Computer Interface for Communication Using Real-Time fMRI
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Choosing parameters for random subspace ensembles for fMRI classification
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Semi-supervised ensemble update strategies for on-line classification of fMRI data
Pattern Recognition Letters
| Hi-index | 0.00 |
Advances in Eelectroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have opened up the possibility for real time data classification. A small amount of labelled training data is usually available, followed by a large stream of unlabelled data. Noise and possible concept drift pose a further challenge. A fixed pre-trained classifier may not always work. One solution is to update the classifier in real-time. Since true labels are not available, the classifier is updated using the predicted label, a method called naive labelling. We propose to use classifier ensembles in order to counteract the adverse effect of ‘run-away' classifiers, associated with naive labelling. A new ensemble method for naive labelling is proposed. The label taken to update each member-classifier is the ensemble prediction. We use an fMRI dataset to demonstrate the advantage of the proposed method over the fixed classifier and the single classifier updated through naive labelling.