Advances in Algorithms for Re Sampling Class Imbalanced Educational Data Sets
Real world data sets often contain disproportionate sample sizes of observed groups making it difficult for predictive analytics algorithms. One of the many ways to combat inherent bias from class imbalance data is to perform re sampling. In this book chapter we discuss popular re sampling methods proposed in research literature, such as Synthetic Minority Over sampling Technique (SMOTE) and Propensity Score Matching (PSM) We provide an insight into recent advances and our own novel algorithms under the umbrella term of Over sampling Using Propensity Scores (OUPS) Using simulation we conduct experiments that result in statistical improvement in accuracy and sensitivity by using these new algorithmic approaches.