Imbalanced Ensemble Classifier for learning from imbalanced business school data set

TL;DR

This paper introduces an ensemble classifier designed to effectively handle imbalanced datasets in business school student placement prediction, demonstrating improved accuracy on real Indian data.

Contribution

It proposes a novel imbalanced ensemble classifier that enhances prediction accuracy for imbalanced business school datasets, with optimized model parameters and empirical validation.

Findings

The classifier outperforms existing methods in accuracy.

Optimal model parameters improve performance.

Numerical results confirm the effectiveness of the approach.

Abstract

Private business schools in India face a common problem of selecting quality students for their MBA programs to achieve the desired placement percentage. Generally, such data sets 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. Numerical evidence is also provided using Indian business school dataset to assess the outstanding performance of the proposed classifier.