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- W4367360437 abstract "This paper aims to find a representation of KDD cup 2015 dataset for predicting MOOCs student dropout in a reduced space, using dimensionality reduction methods. Dimensionality reduction techniques intend to overcome the curse of dimensionality by providing an effective solution to this problem leading to machine learning algorithms inefficiency. In this work, we used two dimensionality reduction techniques: features selection and data transformation. Our technique uses the contribution of many features selection methods in order to find the pertinent subset of features to keep. The obtained results comparing the models accuracy of the initial dataset and the subset of selected features encourage the use of these methods in order to reduce the complexity of the problem and the computation time." @default.
- W4367360437 created "2023-04-30" @default.
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- W4367360437 date "2023-01-01" @default.
- W4367360437 modified "2023-09-26" @default.
- W4367360437 title "Dimensionality Reduction for Predicting Students Dropout in MOOC" @default.
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- W4367360437 doi "https://doi.org/10.1007/978-3-031-29857-8_43" @default.
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