Publication

Abstract : Imbalanced distribution of instances across the classes is a challenging issue when the underlying problem is of type classification. The reason is that classifiers will tend to favor the classes with a large number of instances i.e. instances of minority classes may be identified as instances of majority classes by the classifiers. In recent years, plenty of researches have been done to resolve the class imbalance issue in binary classification problems which resulted in many class imbalance learning techniques for binary classification problems. But, the class imbalance in multi-class classification problems did not draw much attention from the research community. Unlike binary class imbalance learning, multi-class imbalance learning techniques experience more than one majority class and more than one minority class. This paper tries to come up with a multi-class imbalanced learning technique that can overcome the effects of multi-class imbalance problem in review rating prediction tasks. The proposed model handles the multi-class imbalance issue by using the combination of hybrid sampling and ensemble learning techniques. Sampling techniques such as Random Under Sampling (RUS) and Synthetic Minority Over-sampling TEchnique(SMOTE) are jointly used in the proposed model to create balanced training sets for base learners. Also, the proposed model creates a powerful ensemble structure by amalgamating a manually created bagging ensemble and AdaBoost boosting ensembles. Experiments are done using the Amazon product dataset in order to investigate the performance of the proposed model. The experimental results show that the proposed Class Imbalance-Aware Review rating prediction(CIAR) model outperforms almost all the baseline models in-terms of G-mean, F-Score, and ROC_AUC_Score.