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Epileptic seizure prediction, with at least some minutes in advance, would improve substantially the quality of life of patients with refractory epilepsy. This is addressed as a classification problem were the brain state is classified using a number of features extracted from the EEG. Methods based on computational intelligence, like support vector machines (SVM), are applied to build up classifiers...
Using Boolean AND and OR functions to combine the responses of multiple one- or two-class classifiers in the ROC space may significantly improve performance of a detection system over a single best classifier. However, techniques found in literature assume that the classifiers are conditionally independent, and that their ROC curves are convex. These assumptions are not valid in most real-world applications,...
The process of categorizing packets into flows in an internet router is called packet classification. All packets belonging to the same flow obey pre-defined rules and are processed in a similar manner by the router. Packet classification is needed for non best-effort services, such as firewalls and quality of service, services that require the capability to distinguish and isolate traffic in different...
In this paper, we approach the problem of constructing ensembles of classifiers from the point of view of instance selection. Instance selection is aimed at obtaining a subset of the instances available for training capable of achieving, at least, the same performance as the whole training set. In this way, instance selection algorithms try to keep the performance of the classifiers while reducing...
Classification cascade is a well-known technique to reduce classification complexity (recognition time) while attaining high accuracy. While cascades are usually built using ad-hoc procedures, in this paper we introduce a principle way of building cascades using a greedy approach. Given a large pool of classifiers, our approach sequentially builds a near-to-optimal cascade. The approach is fully automated,...
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