169 citations · 180 across the 4 of their papers we have counts for
4 papers
Class-Imbalanced Graph Learning without Class Rebalancing
Zhining Liu, Ruizhong Qiu, Zhichen Zeng +7
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (…
UADB: Unsupervised Anomaly Detection Booster
Hangting Ye, Zhining Liu, Xinyi Shen +6
Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to its wide real-world applications. Due to the complete absence of supervision signals, UAD methods rely on…
Handling Inter-class and Intra-class Imbalance in Class-imbalanced Learning
Zhining Liu, Pengfei Wei, Zhepei Wei +5
Class-imbalance is a common problem in machine learning practice. Typical Imbalanced Learning (IL) methods balance the data via intuitive class-wise resampling or reweighting. Howe…
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
Zhining Liu, Wei Cao, Zhifeng Gao +4
Many real-world applications reveal difficulties in learning classifiers from imbalanced data. The rising big data era has been witnessing more classification tasks with large-scal…