Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …

Traditional classification algorithms can be limited in their performance on highly
unbalanced data sets. A popular stream of work for countering the problem of class …

Canonical machine learning algorithms assume that the number of objects in the considered
classes are roughly similar. However, in many real-life situations the distribution of examples …

Learning with imbalanced data is one of the recent challenges in machine learning. Various
solutions have been proposed in order to find a treatment for this problem, such as …

For imbalanced datasets, the focus of classification is to identify samples of the minority
class. The performance of current data mining algorithms is not good enough for processing …

The tremendous amount of data is generated regularly through areas like networking,
telecommunication, stock market, satellite, weather forecasting, etc. So, the classification …

Time series prediction, especially financial time series prediction, is a challenging task in
machine learning. In this issue, the data are usually non-stationary and volatile in nature …

Gravitation is a fundamental interaction whose concept and effects applied to data
classification become a novel data classification technique. The simple principle of data …

When an intelligent model is used for medical diagnosis, it is desirable to have a high level
of interpretability and transparent model reliability for users. Compared with most of the …