Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from
achieving satisfactory results. ID is the occurrence of a situation where the quantity of the …

Class imbalance data sets are common in a vast variety of real-world application areas.
Synthetic minority oversampling technique (SMOTE) is an important technique for …

Heider's structural balance theory has proven invaluable in comprehending the dynamics of
social groups characterized by both friendly and hostile relationships. Since people's …

In a complex classification task, samples are represented by various types of multimodal
features, including structured data, text, images, video, audio, etc. These data are usually …

Structural balance has been posited as one of the factors influencing how friendly and
hostile relations of social actors evolve over time. This study investigates the behavior of the …

Multi-kernel learning methods are essential kernel learning methods. Still, the base kernel
functions in most multi-kernel learning methods only with select kernel functions with …

**年来, 多核方法已被证实在很多领域上有着比单核更好的性能. 然而, 现有的在线多标签分类
算法大多采用单核方法, 并且依赖于离线的核函数选择过程. 为了克服这些问题并提升分类性能 …