Identifying Indistinguishable Classes in Multi-class Classification Data Sets Using ELM

Abstract

For many years, various neural network models have been used to solve regression, binary classification, and multi-class classification problems. Their performance has been extensively compared against each other in terms of testing accuracy and training time. For multi-class classification problem, testing accuracy does not always give comprehensive information about the performance. It only shows the number of false detections without any clues on the false detection distribution. In this work we propose a cross-validation based on Extreme Learning Machine to identify classes that are found to have high number of false detections. These classes are treated as indistinguishable classes that need further processing or information. Our simulation shows that our proposed method is able to detect indistinguishable classes in three data sets. We also found that when indistinguishable classes exist, the training accuracy can be higher if each pair of those classes are marked as one merged class.