For the last two decades, oversampling has been employed to overcome the challenge of
learning from imbalanced datasets. Many approaches to solving this challenge have been …

Class imbalance is a key issue affecting the performance of computer vision applications
such as medical image analysis, objection detection and recognition, image segmentation …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

Existing water quality index (WQI) models assess water quality using a range of
classification schemes. Consequently, different methods provide a number of interpretations …

Here, we present an improved water quality index (WQI) model for assessment of coastal
water quality using Cork Harbour, Ireland, as the case study. The model involves the usual …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

In marine ecosystems, both living and non-living organisms depend on “good” water quality.
It depends on a number of factors, and one of the most important is the quality of the water …

Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …

Abstract The Water Quality Index (WQI) is a primary metric used to evaluate and categorize
surface water quality which plays a crucial role in the management of fresh water resources …

Multi-class imbalanced classification is more difficult and less frequently studied than its
binary counterpart. Moreover, research on the causes of the difficulty of multi-class …