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This study is motivated by the magnitude of the problem of Louisiana highschool dropout and its negative impacts on individual and public well-being.Our goal is to predict students who are at risk of high school dropout, byexamining Louisiana administrative dataset. Due to the imbalanced nature of thedataset, imbalanced learning techniques including resampling, case weighting,and cost-sensitive learning have been applied to enhance the predictionperformance on the rare class. Performance metrics used in this study areF-measure, recall and precision of the rare class. We compare the performanceof several machine learning algorithms such as neural networks, decision treesand bagging trees in combination with the imbalanced learning approaches usingan administrative dataset of size of 366k+ from Louisiana Department ofEducation. Experiments show that application of imbalanced learning methodsproduces good results on recall but decreases precision, whereas baseclassifiers without regard of imbalanced data handling gives better precisionbut poor recall. Overall application of imbalanced learning techniques isbeneficial, yet more studies are desired to improve precision.

Predicting Louisiana Public High School Dropout through Imbalanced Learning Techniques