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arXiv Machine Learning By Dang Nguyen, Bao Duong, Arun Kumar, Dat Phan-Trong, Julian Berk, Taylor Braund, Kien Do, Debopriyo Bal, Wu Yi Zheng, Leonard Hoon, Jill Newby, Helen Christensen, Svetha Venkatesh, Alexis Whitton, Sunil Gupta

Predicting Symptoms of Amotivation and Anhedonia among University Students with a Novel Oversampling Method

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The paper introduces a new oversampling technique that uses a predictive model to generate nominal variables instead of interpolating them, addressing a key limitation of SMOTE. Applied to a large GPS‑location dataset of university students, the method outperforms existing oversampling approaches in predicting high levels of amotivation and anhedonia. This improvement could help identify students with these debilitating symptoms earlier and more accurately.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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