Experimental Comparison of Sampling Techniques for Imbalanced Datasets Using Various Classification Models

Imbalanced dataset is a dataset, in which the number of samples in different classes is highly uneven, which makes it very challenging for classification, i.e., classification becomes very tough as the result may get biased by the dominating class values. But misclassification of minor class sample or interested samples is very much costlier. So to provide solution to this problem, various studies have been made out of which sampling techniques are successfully adopted to preprocess the imbalance datasets. In this paper, experimental comparison of two pioneering sampling techniques SMOTE and MWMOTE is simulated using the classification models SVM, RBF, and MLP.