Enhanced SMOTE algorithm for classification of imbalanced big-data using Random Forest

In the era of big data, the applications generating tremendous amount of data are becoming the main focus of attention as the wide increment of data generation and storage that has taken place in the last few years. This scenario is challenging for data mining techniques which are not arrogated to the new space and time requirements. In many of the real world applications, classification of imbalanced data-sets is the point of attraction. Most of the classification methods focused on two-class imbalanced problem. So, it is necessary to solve multi-class imbalanced problem, which exist in real-world domains.