# An Empirical Assessment of Performance of Data Balancing Techniques in Classification Task
## 5 Conclusions and Recommendation for Further Work  
In this research paper, we have assessed the performance of six different DBT. The assessment was performed using six different classifiers and 25 different datasets that had different levels of IR. The performance of the DBT was assessed using the performance of classifiers, which was measured using the area under ROC curve. The experimental results show that (i) for all the six classifiers, the performance of the None and ROSE strategy was poor and significantly different than the others. It was also observed that there was no significant difference in the performance of the US, OS, HS, SMOTE, and CBUS techniques; (ii) performance of None and ROSE was poor and consistent across the classifiers. There was no consistency in the performance of US, OS, HS, SMOTE, and CBUS techniques, but its performance was better than the ROSE and None strategy; (iii) there was no agreement over the ranks of DBT for varying levels of IR in the dataset except for the None and ROSE strategy; (iv) DBT helps to improve the performance of the classifiers. However, performance of the ROSE was not significantly different than the None Strategy. Thus, from the experimental results, we may infer that DBT helps to improve the performance of the classifier in classification tasks. Further, performance of the DBT is independent of the classification algorithm and IR in the dataset. These inferences are drawn based on our experimental results. As stated earlier in the introduction section, we assessed the performance of only data-preprocessing-level data-balancing techniques. However, there is a need to assess the performance of advanced DBT such as algorithm-level solutions, cost-based learning, and ensemble methods. Conceptualization, A.J. and S.M.M. ; methodology, A.J. and S.M.M. ; software, A.J. and S.M.M. ; validation, A.J. and S.M.M. ; formal analysis, A.J. and S.M.M. ; investigation, A.J., H.E., F.K.K. and S.M.M. ; resources, A.J., H.E., F.K.K. and S.M.M. ; data curation, A.J. and S.M.M. ; writing--original draft preparation, A.J., H.E., F.K.K. and S.M.M. ; writing--review and editing, A.J., H.E., F.K.K. and S.M.M. ; visualization, A.J., H.E., F.K.K. and S.M.M. ; supervision, H.E., F.K.K. and S.M.M. ; project administration, H.E. and F.K.K. ; funding acquisition, H.E. and F.K.K. All authors have read and agreed to the published version of the manuscript. This research project was funded by [PERSON] University Researchers Supporting Project number (PNURSP2022R300). Not applicable. The data presented in this study are available on request. This research project was funded by [PERSON] Researchers Supporting Project Researchers Supporting Project number (PNURSP2022R300). Not applicable. This research project was funded by [PERSON] University Researchers Supporting Project number (PNURSP2022R300). * [PERSON] (2018) [PERSON] Clustering Based Data Preprocessing Technique to Deal with Imbalanced Dataset Problem in Classification Task. In Proceedings of the IEEE Punecon, Pune, India, 30 November-2 December 2018; pp. 1-7. * [PERSON] et al. (1999) [PERSON]; [PERSON]; [PERSON]; [PERSON] AdaCost: Misclassification cost-sensitive boosting. In Proceedings of the Sixteenth International Conference on Machine Learning, San Francisco, CA, USA, 27-30 June 1999; pp. 99-105. * [PERSON] and [PERSON] (2006) [PERSON]; [PERSON] Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. _IEEE Trans. Knowl. Data Eng._**2006**, _18_, 63-77. [CrossRef]
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