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Advances in Algorithms for Re-Sampling Class-Imbalanced Educational Data Sets
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Author(s): William Rivera (Institute for Simulation and Training, University of Central Florida, USA), Amit Goel (Institute for Simulation and Training, University of Central Florida, USA)and J Peter Kincaid (Institute for Simulation and Training, University of Central Florida, USA)
Copyright: 2017
Pages: 31
Source title:
Artificial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-1759-7.ch040
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Abstract
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.
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