A novel data reduction method: Distance based data reduction and its application to classification of epileptiform EEG signals

摘要

ObjectiveData reduction methods are a crucial step affecting both performance and computation time of classification systems in pattern recognition applications such as medical decision making systems, intelligent control, and data clustering. The aim of this study is both to increase the classification accuracy and decrease the computation time of classifier system on the classification of epileptiform EEG signals.