Under-bagging Nearest Neighbors for Imbalanced Classification

：蔡宇超（荷兰特文特大学）
：2024-01-06 10:30
：海韵园数理大楼686会议室

报告人：蔡宇超（荷兰特文特大学）

时  间：2024年1月6日10:30

地  点：海韵园数理大楼686会议室

内容摘要:

In this presentation, we will talk about an ensemble learning algorithm called under-bagging k-nearest neighbors (under-bagging k-NN) for imbalanced classification problems. On the theoretical side, by developing a new learning theory analysis, we show that with properly chosen parameters, i.e., the number of nearest neighbors k, the expected sub-sample size s, and the bagging rounds B, optimal convergence rates for under-bagging k-NN can be achieved under mild assumptions w.r.t. the arithmetic mean (AM) of recalls. Moreover, we show that with a relatively small B, the expected sub-sample size s can be much smaller than the number of training data n at each bagging round, and the number of nearest neighbors k can be reduced simultaneously, especially when the data are highly imbalanced, which leads to substantially lower time complexity and roughly the same space complexity. On the practical side, we conduct numerical experiments to verify the theoretical results on the benefits of the under-bagging technique by the promising AM performance and efficiency of our proposed algorithm. 

个人简介：

蔡宇超，2018年在厦门大学数学科学学院获得理学学士学位，2023年在中国人民大学统计学院获得统计学博士学位。毕业后在特文特大学统计系从事博士后研究。主要研究方向为统计机器学习，统计学习理论，集成学习，无监督学习。研究成果发表在机器学习顶会ICML，NeurIPS和国际机器学习顶刊JMLR。

 

联系人：周达