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- W2023359546 abstract "The goal of the paper is to predict student retention by using linear discriminant analysis with bootstrapping. The result (93%) provides accuracy superior to the bootstrapping of a comparative method, as well as to the non-bootstrapping variations. In order to perform discriminant analysis, we linearize a fractional programming method by using Charnes-Cooper transformation and apply linear programming, while the comparative approach uses deviation variables to tackle a similar multiple criteria optimization problem. We train the discriminatory hyperplane family and apply it to the testing set - thus arriving at a set of histograms. We analyze the histograms by using the simple mean - best for prediction - and a five-fold Kolmogorov-Smirnov test - best used for resources allocation, in order to act on the final results. Final results are the outcome of applying the hyperplane family on freshman data." @default.
- W2023359546 created "2016-06-24" @default.
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- W2023359546 date "2013-09-01" @default.
- W2023359546 modified "2023-09-24" @default.
- W2023359546 title "Predicting student retention by comparing histograms of bootstrapping for Charnes-Cooper transformationlinear programming discriminant analysis" @default.
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- W2023359546 doi "https://doi.org/10.1109/icelete.2013.6644357" @default.
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