The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage
numerous existing high-performing models instead of building machine learning models …

Multi-class imbalance problems are frequently encountered in real-world applications of
machine learning. They have fundamentally complex trade-offs between classes. Existing …

Selective classification enables models to make predictions only when they are sufficiently
confident, aiming to enhance safety and reliability, which is important in high-stakes …

Ensemble methods that train and combine multiple learners have always been among the
state-of-the-art learning methods, and ensemble pruning aims at generating a smaller-sized …

Ensemble pruning that combines a subset of individual learners generated in parallel to
make predictions is an important topic in ensemble learning. Past decades have developed …

Multi-label active learning has garnered significant attention due to its potential to make use
of unlabeled data with limited labeling budget by selectively querying the most valuable …

Multi-label classification is an important topic in machine learning, where each instance can
be classified into more than one category, ie, have a subset of labels instead of only one …