Broad learning system (BLS) is a novel and efficient model, which facilitates representation
learning and classification by concatenating feature nodes and enhancement nodes. In spite …

The class imbalance problem has become a leading challenge. Although conventional
imbalance learning methods are proposed to tackle this problem, they have some …

The class imbalance problem has posed a leading challenge in real-world applications.
Traditional methods focus on either the data level or algorithm level to solve the binary …

As one of the most challenging and attractive problems in the pattern recognition and
machine intelligence field, imbalanced classification has received a large amount of …

The class-imbalance problem has been widely distributed in various research fields. The
larger the data scale and the higher the data imbalance, the more difficult the proper …

With the expansion of data, increasing imbalanced data has emerged. When the imbalance
ratio of data is high, most existing imbalanced learning methods decline in classification …

In recent years, data imbalance in the conventional classification problem has raised great
interest in the industry. However, concerning the rule-based systems, this problem has been …

It is hard to construct an optimal classifier for high-dimensional imbalanced data, on which
the performance of classifiers is seriously affected and becomes poor. Although many …

Highly imbalanced class distribution has been well-recognized as a major cause of
performance degradation for most supervised learning algorithms. Unfortunately, such …

Imbalanced learning is the issue of learning from data when the class distribution is highly
imbalanced. Class imbalance problems are seen increasingly in many domains and pose a …