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Kappa-error diagrams are used to gain insights about why an ensemble method is better than another on a given data set. A point on the diagram corresponds to a pair of classifiers. The x-axis is the pairwise diversity (kappa), and the y-axis is the averaged individual error. In this study, kappa is calculated from the 2\times2 correct/wrong contingency matrix. We derive a lower bound on kappa which...
Developing accurate, reliable and easy to use diagnostic tests is based upon identifying a small set of highly discriminative biomarkers. This task can be cast as feature selection within a pattern recognition context. Medical data are usually of the "wide" type where the number of features is substantially larger than the number of instances. With the abundance of feature ranking methods,...
While there is a lot of research on change detection based on the streaming classification error, finding changes in multidimensional unlabelled streaming data is still a challenge. Here we propose to apply principal component analysis (PCA) to the training data, and mine the stream of selected principal components for change in the distribution. A recently proposed semi-parametric log-likelihood...
The advent of real-time fMRI pattern classification opens many avenues for interactive self-regulation where the brain's response is better modelled by multivariate, rather than univariate techniques. Here we test three on-line linear classifiers, applied to a real fMRI dataset, collected as part of an experiment on the cortical response to emotional stimuli. We propose a random subspace ensemble...
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