Adaptive Learning in Imbalanced Data Streams With Unpredictable Feature Evolution.

Adaptive Learning for Dynamic Features and Noisy Labels.

Learning With Incremental Instances and Features.

Multi-Instance Learning with One Side Label Noise.

Feature incremental learning with causality.

Online imbalance learning with unpredictable feature evolution and label scarcity.

Online Learning With Incremental Feature Space and Bandit Feedback.

Incremental Learning for Simultaneous Augmentation of Feature and Class.

Incremental Feature Spaces Learning with Label Scarcity.

Fragmentary label distribution learning via graph regularized maximum entropy criteria.

Active label distribution learning.

Active learning with error-correcting output codes.

Recursive Maximum Margin Active Learning.