Imbalanced data poses a substantial challenge to conventional classification methods,
which often disproportionately favor samples from the majority class. To mitigate this issue …

The problem of class imbalance is pervasive across various real-world applications,
resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm …

Mineral prospectivity map** is crucial for identifying areas with economically valuable
minerals. Therefore, several methods based on machine learning have been applied to …

Time series anomaly detection is the process of identifying anomalies within time series
data. The primary challenge of this task lies in the necessity for the model to comprehend the …

Due to the advantages of fast training speed and competitive performance, Broad Learning
System (BLS) has been widely used for classification tasks across various domains …

Groundwater pollution has become a global challenge, posing significant threats to human
health and ecological environments. Machine learning, with its superior ability to capture …

Credit evaluation has always been an important part of the financial field. The existing credit
evaluation methods have difficulty in solving the problems of redundant data features and …

The transition to smart grids has served to transform traditional power systems into
datadriven power systems. The purpose of this transition is to enable effective energy …

High accuracy prediction of urban flood risk is conducive to avoid potential losses, however,
it's negatively affected by unbalanced data. Furthermore, ensemble model has been …

Mechanical activities near energy pipelines pose a significant threat to energy transportation
safety and energy system supply. The distributed acoustic sensing (DAS) system, which is …