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cs.LG2023★ 2 cited
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 (…
cs.LG2023
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…
cs.LG2019
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…