Imbalanced Ensemble Classifier for learning from imbalanced business school data set
This addresses the specific issue of student selection for MBA programs in Indian business schools, but it is incremental as it applies known imbalanced learning techniques to a new domain dataset.
The paper tackles the problem of learning from imbalanced business school datasets in India by proposing an imbalanced ensemble classifier, which achieves higher accuracy for feature selection and classification tasks related to student placement prediction.
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.