Benchmarking framework for class imbalance problem using novel sampling approach for big data

The traditional techniques of machine learning always need to be strengthened for dealing with cosmic nature of big data for systematic and methodical learning. The unbalanced distribution of classes in big data, popularly known as imbalanced big data chases the problem of learning to a much higher level. The conventional methods are being progressively modified to handle and curtail the problem of learning from imbalanced datasets in the context of big data at the data level and algorithmic level. In the current study, a cluster heads based data level sampling solution which inherits edge of K-Means and Fuzzy C-Means clustering approaches is applied. The proposed approach is evaluated with three different classifiers namely Support Vector Machines, Decision Tree and k-Nearest Neighbor and compared with conventional SMOTE algorithm. The experiment has shown promising results with an increment of 8.09% and 35.71% in terms of accuracy and AUC respectively, for all imbalanced datasets. This work imparts a baseline comparison of solutions for imbalanced classification at data level in big data scenario and proposes an efficient clustering-based solution for same.