Background Imbalance between positive and negative outcomes, a so-called class
imbalance, is a problem generally found in medical data. Despite various studies, class …

In IoT environment applications generate continuous non-stationary data streams with in-
built problems of concept drift and class imbalance which cause classifier performance …

With rapid technological progress in the Internet of Things (IoT), it has become imperative to
concentrate on its security aspect. This paper represents a model that accounts for the …

Machine learning technology has a massive impact on society because it offers solutions to
solve many complicated problems like classification, clustering analysis, and predictions …

The robustness of some classical univariate classifiers is hampered if the data are
contaminated. Overfitting is another hiccup when the data sets are uncontaminated with a …

Learning Vector Quantization (LVQ) is one of the most widely used classification
approaches. LVQ faces a problem as when the size of data grows large it becomes slower …

Crime forecasting is beneficial as it provides valuable information to the government and
authorities in planning an efficient crime prevention measure. Most criminology studies …

Computer-aided vibrational spectroscopy detection technology has achieved promising
results in the field of early disease diagnosis. Yet limited by factors such as the number of …

This survey explores recent advances in addressing class imbalance issues for developing
machine learning models in precision agriculture, with a focus on techniques used for plant …

The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances
robustness and clustering accuracy through the ordered mechanism based on fuzzy C …