Imbalanced Ensemble Classifier for learning from imbalanced business school data set

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

Local case-control sampling: Efficient subsampling in imbalanced data sets

For classification problems with significant class imbalance, subsampling can reduce computational costs at the price of inflated variance in estimating model parameters. We propose a method for subsampling efficiently for logistic regression by adjusting the class balance locally in feature space via an accept-reject scheme. Our method generalizes standard case-control sampling, using a pilot estimate to preferentially select examples whose responses are conditionally rare given their features. The biased subsampling is corrected by a post-hoc analytic adjustment to the parameters. The method is simple and requires one parallelizable scan over the full data set. Standard case-control sampling is inconsistent under model misspecification for the population risk-minimizing coefficients $\theta^*$. By contrast, our estimator is consistent for $\theta^*$ provided that the pilot estimate is. Moreover, under correct specification and with a consistent, independent pilot estimate, our estimator has exactly twice the asymptotic variance of the full-sample MLE - even if the selected subsample comprises a miniscule fraction of the full data set, as happens when the original data are severely imbalanced. The factor of two improves to $1+\frac{1}{c}$ if we multiply the baseline acceptance probabilities by $c>1$ (and weight points with acceptance probability greater than 1), taking roughly $\frac{1+c}{2}$ times as many data points into the subsample. Experiments on simulated and real data show that our method can substantially outperform standard case-control subsampling.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1220 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org