AI and Intelligent Systems: Engineering, Medicine & Society(AIISEMS)

Tay Do University, Tran Chien Street, Thanh My Area, Cai Rang Ward, Can Tho City, Vietnam

Publications

• Review Article   
  A Multi-Model Ensemble Framework for March Machine Learning Mania: Leveraging Gradient Boosting and Statistical Efficiency Metrics
  Author(s): Trinh Quang Minh*, Ngo Thi Lan, Nguyen Minh Hieu, Tran Minh Tan, Do Chi Tam, Lam Tan Phuong, Nguyen Chi Cuong and Bui Xuan Tung
  
  Forecasting outcomes in collegiate basketball tournaments is inherently difficult due to the volatility and non-linear dynamics of team performance. This paper introduces a predictive framework designed for the March Machine Learning Mania 2026 competition, addressing both Men's and Women's NCAA tournaments. By applying advanced statistical feature engineering, the study moves beyond simple win-loss records to derive refined metrics such as possession- based offensive and defensive efficiency, effective field goal percentage (eFG), and pace. At the core of the framework is a weighted ensemble model that integrates three complementary machine learning algorithms: Logistic Regression, XGBoost, and Random Forest. Optimized weights—20% for Logistic Regression, 50% for XGBoost, and 30% for Random Forest—enable the ensemble to combine the interpretability of regression w.. Read More»
  

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