Analysing the safe, average and border samples on two-class imbalance problems in the back-propagation domain

Abstract

In this work, we analyze the training samples for discovering what kind of samples are more appropriate to train the back-propagation algorithm. To do this, we propose a Gaussian function in order to identify three types of samples: Border, Safe and Average samples. Experiments on sixteen two-class imbalanced data sets where carried out, and a non-parametrical statistical test was applied. In addition, we employ the SMOTE as classification performance reference, i.e., to know whether the studied methods are competitive with respect to SMOTE performance. Experimental results show that the best samples to train the back-propagation are the average samples and the worst are the safe samples.