MiNB: Minority Sensitive Naïve Bayesian Algorithm for Multi-Class Classification of Unbalanced Data

The unbalanced nature of data makes it tough to achieve the desire performance goal for classification algorithms. The sub-optimal prediction system isn't a viable solution due to the high misclassification cost of minority events. Thus accurate imbalanced data classification could be a path changer for prediction in domains like medical diagnosis, judiciary, and disaster management systems. To date, most of the existing studies of imbalanced data are for the binary class dataset and supported by data sampling techniques that suffer from loss of information and over-fitting. In this paper, we present the modified naïve Bayesian algorithm for unbalanced data classification that eliminates the requirement of data level sampling. We compared our proposed model with the data sampling technique and cost-sensitive techniques. We use minority sensitive TP Rate, class-specific misclassification rate, and overall performance parameters such as accuracy, f-measure and G-mean. The result shows that our proposed algorithm shows a more optimal result for unbalanced data classification. Results shows reduction in misclassification rate and improve predictive performance for the minority class.