范宗文 华侨大学计算机科学与技术学院讲师。主要研究方向为：机器学习，模糊系统，数据挖掘。

参与国家级项目：国家自然科学基金面上项目，61572204，具有个体差异进化和冲突检测消解机制的多源模糊知识融合方法研究，2016/01/01 至 2019/12/31，已结题

近三年科研成果：

[1]   Zongwen Fan, Jin Gou, Shaoyuan Weng. A Feature Importance-Based Multi-Layer CatBoost for Student Performance Prediction. IEEE Transactions on Knowledge and Data Engineering, 36(11), 5495-5507, 2024.

[2]   Zongwen Fan, Jin Gou, and Shaoyuan Weng. Complementary CatBoost based on residual error for student performance prediction. Pattern Recognition, 2025, 161, 111265.

[3]   Zongwen Fan, Jin Gou, Shaoyuan Weng. A Novel Fuzzy Feature Generation Approach for Happiness Prediction, IEEE Transactions on Emerging Topics in Computational Intelligence, 8(2), 1595-1608, 2024

[4]   Zongwen Fan, Jin Gou, Shaoyuan Weng. An Unbiased Fuzzy Weighted Relative Error Support Vector Machine for Reverse Prediction of Concrete Components. IEEE Transactions on Artificial Intelligence, 5(9), 4574-4584, 2024.

[5]   Zongwen Fan, Shaleeza Sohail, Fariza Sabrina, and Xin Gu. Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset. Electronics, 2024, 13(10), 1878.

[6]   Raymond Chiong, Zongwen Fan*. Zhongyi Hu, Sandeep Dhakal, A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method, IEEE Transactions on Computational Social Systems, 2023, 10(5): 2613-2623

[7]   Zongwen Fan, Jin Gou. Predicting body fat using a novel fuzzy-weighted approach optimized by the whale optimization algorithm, Expert Systems with Applications, 2023, 217:119558

[8]   Zongwen Fan, Jin Gou, Cheng Wang. Predicting secondary school student performance using a double particle swarm optimization-based categorical boosting model, Engineering Applications of Artificial Intelligence, 2023, 124: 106649

[9]   Zongwen Fan, Fenlin Wu, Yaxuan Tang. A hierarchy-based machine learning model for happiness prediction, Applied Intelligence, 2023, 53: 7108-7117

[10] Zongwen Fan, and Raymond Chiong. Identifying digital capabilities in university courses: An automated machine learning approach. Education and Information Technologies, 2023, 28(4), 3937-3952.