Generalization Performance of Pure Accuracy and Its Application in Selective Ensemble Learning

Jieting Wang, Yuhua Qian, Feijiang Li, Jiye Liang, Qingfu Zhang

The pure accuracy measure is used to eliminate random consistency from the accuracy measure. Biases to both majority and minority classes in the pure accuracy are lower than that in the accuracy measure.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023,45(2),1798-1816

Incremental Learning for Simultaneous Augmentation of Feature and Class

Chenping Hou,Shilin Gu, Chao Xu, Yuhua Qian

With the emergence of new data collection ways in many dynamic environment applications, the samples are gathered gradually in the accumulated feature spaces.

IEEE Transactions on Pattern Analysis and Machine Intelligence,DOI 10.1109/TPAMI.2023.3307670

ESSR: Evolving Sparse Sharing Representation for Multi-task Learning

Yayu Zhang,Yuhua Qian,Guoshuai Ma, Xinyan Liang, Guoqing Liu, Qingfu Zhang,Ke Tang

Abstract—Multi-task learning uses knowledge transfer among tasks to improve the generalization performance of all tasks. For deep multi-task learning, knowledge transfer is often implemented via sharing all hidden features of tasks.

IEEE Transactions on Evolutionary Computation,2023,DOI 10.1109/TEVC.2023.3272663

Learning With Incremental Instances and Features

Shilin Gu , Yuhua Qian, Chenping Hou

In many real-world applications, data may dynamically expand over time in both volume and feature dimensions. Besides, they are often collected in batches (also called blocks).

IEEE Transactions on Neural Networks and Learning Systems,2023,In Press

缓解随机一致性的基尼指数与决策树方法

王婕婷,李飞江,李珏,钱宇华,梁吉业

决策树模型具有较强的可解释性, 是随机森林、深度森林等机器学习方法的基础. 如何选择节点的分割属性与分割值是决策树算法的关键问题, 对树的泛化能力、深度、平衡程度等重要性能产生影响.

中国科学:信息科学, 2024, doi: 10.1360/ SSI-2022-0337