The problem of imbalanced data classification often exists in medical diagnosis. Traditional
classification algorithms usually assume that the number of samples in each class is similar …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

The Adaptive Boosting (AdaBoost) algorithm is a widely used ensemble learning framework,
and it can get good classification results on general datasets. However, it is challenging to …

With the economy rapid development, the credit card business enjoys sustained growth,
which leads to the frauds happen frequently. Recent years, the intelligence technology has …

In supervised learning, the efficacy of classifier algorithms is heavily dependent on the
quality of data. Imbalanced datasets, where the class distribution is not uniform, pose a …

Class imbalance problem is prevalent in many real-world domains. It has become an active
area of research. In binary classification problems, imbalance learning refers to learning …

Classification of imbalanced big data has assembled an extensive consideration by many
researchers during the last decade. Standard classification methods poorly diagnosis the …

Classifier design is one of the most significant fields in pattern recognition. Most classifiers
are measured by classification accuracy, which assumes that all the misclassification cost …

With the rapid development of artificial intelligence, automated endoscopy-assisted
diagnostic systems have become an effective tool for reducing the diagnostic costs and …