As an important part of machine learning, classification learning has been applied in many
practical fields. It is valuable that to discuss class imbalance learning in several fields. In this …

Balancing the accuracy rates of the majority and minority classes is challenging in
imbalanced classification. Furthermore, data characteristics have a significant impact on the …

Recently, with the development of technology, it is quite important to study scalable
computational methods for handling large-scale data in big data applications. The …

With the emphasis on preventing urban flooding and the enhancement of rational urban
development, data related to urban flooding are also collected with unbalanced sample size …

Imbalanced data classification is a challenge in data mining and machine learning. To
improve the classification performance for imbalanced data, this paper proposes an …

This article considers a category of constrained convex optimization problems over
multiagent networks. The networked agents aim at collaboratively minimizing the sum of all …

Gradient descent methods are simple and efficient optimization algorithms with widespread
applications. To handle high-dimensional problems, we study compressed stochastic …

Image analysis based on biological morphological differences is an important development
direction for classification and determination of planktonic algae. However, it has some …

Post-translational modification (PTM) is crucial for various cell signaling pathways and
biological processes. However, the interactions between PTMs (PTM cross-talk) or PTMs …

Class Imbalance problems are characterized by the presence of a class with a number of
instances that are much smaller (minority class) and other classes with a much larger …