Automatic Neonatal Sleep EEG Recognition with Social Impact Based Feature Selection

The paper presents an application of Simplified Social Impact Theory based Optimization on feature subset selection for automated neonatal sleep EEG recognition. The target classifier is 3-Nearest Neighbor classifier. We also propose a novel initialization of iterative population based optimization heuristics, which is suitable for feature subset selection, because it reduces the computational complexity of whole feature selection process and can help to prevent overfitting problems. Our methods leads to a significant reduction of the original dimensionality while simultaneously reduce the classification error.