Mining rare event classes in noisy EEG by over sampling techniques

Mining is processing data to obtain interesting pattern or knowledge. Noisy EEG can be received on some abnormal state of brain activities. These signals can be logged in data sheets and the samples are taken to identify the rare events. The sampling technique here we used is SMOTE (Synthetic Minority Over-sampling Technique). An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ¿normal¿ patterns with only a small percentage of ¿abnormal¿ or ¿interesting¿ patterns. It is also the case that the cost of misclassifying an abnormal (interesting) pattern as a normal pattern is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class.