Prediction of College Admission Scores Based on an XGBoost-LSTM Hybrid Model

Xu, Liangyu

Prediction of College Admission Scores Based on an XGBoost-LSTM Hybrid Model

Xu, Liangyu (2024) Prediction of College Admission Scores Based on an XGBoost-LSTM Hybrid Model. In: Proceedings of the 3rd International Conference on Educational Innovation and Multimedia Technology, EIMT 2024, March 29–31, 2024, Wuhan, China.

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Abstract

This study introduces a predictive model that combines XGBoost and Long Short-Term Memory (LSTM) networks for forecasting the minimum college admission scores in the Chinese college entrance examination system. By leveraging the strengths of LSTM in handling multivariate time series data and the eff

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Date Deposited:	04 Mar 2026 17:43
Last Modified:	16 Apr 2026 23:13
URI:	http://eprints.eai.eu/id/eprint/49574

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