Tanujit Chakraborty
SQC & OR Unit Indian Statistical Institute, Kolkata, 700108, India.

DOI https://dx.doi.org/10.33889/IJMEMS.2019.4.4-068

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Abstract

Private business schools in India face a regular problem of picking quality students for their MBA programs to achieve the desired placement percentage. Generally, such datasets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper proposes an imbalanced ensemble classifier which can handle the imbalanced nature of the dataset and achieves higher accuracy in case of the feature selection (selection of important characteristics of students) cum classification problem (prediction of placements based on the students’ characteristics) for Indian business school dataset. The optimal value of an important model parameter is found. Experimental evidence is also provided using Indian business school dataset to evaluate the outstanding performance of the proposed imbalanced ensemble classifier.

Keywords- Business school problem, Imbalanced data, Hellinger distance, Ensemble classifier.

Citation

Chakraborty, T. (2019). Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset. International Journal of Mathematical, Engineering and Management Sciences, 4(4), 861-869. https://dx.doi.org/10.33889/IJMEMS.2019.4.4-068.

Conflict of Interest

The author declares that there is no conflict of interest for this publication.

Acknowledgements

The author would like to express his sincere thanks to the referees and editor for their valuable suggestions towards the improvement in the quality of the paper.

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