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Random Forest Missing Data Imputation Methods: Implications for Predicting At-Risk Students

Bevan I. Smith, Charles Chimedza, Jacoba H. Bührmann. Random Forest Missing Data Imputation Methods: Implications for Predicting At-Risk Students. In Ajith Abraham, Patrick Siarry, Kun Ma, Arturas Kaklauskas, editors, Intelligent Systems Design and Applications - 19th International Conference on Intelligent Systems Design and Applications (ISDA 2019), Auburn, WA, USA, December 3-5, 2019. Volume 1181 of Advances in Intelligent Systems and Computing, pages 298-308, Springer, 2019. [doi]

Authors

Bevan I. Smith

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Charles Chimedza

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Jacoba H. Bührmann

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