Data Analytics for Imbalanced Dataset

The primary issue in real-time big data classification is imbalanced datasets. Even though we have many balancing techniques to reduce imbalance ratio which is not suitable for big data that has scalability issues. This study is envisioned to explore different balancing techniques with experimental study. We tried comparing the effectiveness of various balancing strategies, including cutting-edge approaches for severely unbalanced data from online repositories. Here we apply SMOTE, SMOTE ENN and SMOTE Tomek balancing algorithms for dermatology, wine quality and diabetes datasets. After balancing the dataset, the balanced dataset is classified with AdaBoost and random forest algorithms. On three datasets, the outcomes show that the classification algorithm with the balancing technique improves the classification performance for imbalanced datasets. Experiment results showed that the SMOTE ENN technique produces higher classification with accuracy than the SMOTE and SMOTE Tomek techniques. The findings are analyzed with other factors like execution time and scalability. Though SMOTE Tomek produces 1.0 for a few datasets, its execution time is longer than SMOTE ENN. Therefore, SMOTE ENN with random forest classification produces 1.0 accuracy for all three datasets with less execution time. This experimental study analyses to create a novel ensemble technique for balancing highly imbalanced data.