Comparative Analysis of Resampling Techniques under Noisy Imbalanced Datasets

Classification is mainly challenged by the problems in the dataset. When dataset have uneven distribution of data among classes, then class imbalance problem arise. Class imbalance with noise creates immense effect on classification of instances of classes. The main focus of this article is to provide the detail comparative analysis of seven Resampling techniques under 16 noisy imbalanced datasets using C4.5 classifier. The performance evaluation is done by using AUC, F1 score, G-mean. Based on the evaluation, article inferred that SMOTE-ENN perform better than rest of resampling techniques.