Analysis of SMOTE: Modified for Diverse Imbalanced Datasets Under the IoT Environment

The tremendous amount of data generated through IoT can be imbalanced causing class imbalance problem (CIP). CIP is one of the major issues in machine learning where most of the samples belong to one of the classes, thus producing biased classifiers. The authors in this paper are working on four imbalanced datasets belonging to diverse domains. The objective of this study is to deal with CIP using oversampling techniques. One of the commonly used oversampling approaches is synthetic minority oversampling technique (SMOTE). In this paper, the authors have suggested modifications in SMOTE and proposed their own algorithm, SMOTE-modified (SMOTE-M). To provide a fair evaluation, it is compared with three oversampling approaches, SMOTE, adaptive synthetic oversampling (ADASYN), and SMOTE-Adaboost. To evaluate the performances of sampling approaches, models are constructed using four classifiers (K-nearest neighbour, decision tree, naive Bayes, logistic regression) on balanced and imbalanced datasets. The study shows that the results of SMOTE-M are comparable to that of ADASYN and SMOTE-Adaboost.