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 …

Data preprocessing remains an important step in machine learning studies. This is because
proper preprocessing of imbalanced data can enable researchers to reduce defects as …

Machine learning classifiers trained on class imbalanced data are prone to overpredict the
majority class. This leads to a larger misclassification rate for the minority class, which in …

Class imbalance is an important problem, encountered in machine learning applications,
where one class (named as, the minority class) has extremely small number of instances …

Urban air pollution is one of the most critical issues that affect the environment, community
health, economy, and management of urban areas. From a public health perspective, PM …

For the imbalanced classification problems, most traditional classification models only focus
on searching for an excellent classifier to maximize classification accuracy with the fixed …

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 …

This paper presents the methodology that has been used to create the 2011 Area
Classification for Output Areas (2011 OAC). This extends a lineage of widely used public …

Early warning of increased algal activity is important to mitigate potential impacts on aquatic
life and human health. While many methods have been developed to predict increased algal …

The increasingly common occurrence of Mid-Winter Breakups (MWBs) in Canadian rivers,
consisting of the early breakup of ice cover outside of the typical spring season, is a cause …