One of the important issues related with all types of data analysis, either statistical data
analysis, machine learning, data mining, data science or whatever form of data-driven …

Instance selection is becoming increasingly relevant due to the huge amount of data that is
constantly produced in many fields of research. At the same time, most of the recent pattern …

The substantial increase in AI model training has considerable environmental implications,
mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric …

This chapter reviews the data reduction problem for instance and feature selection methods
in the context of supervised classification. In the first part, instance and feature selections are …

In the framework of genetic fuzzy systems, the computational time required by genetic
algorithms for generating fuzzy rule-based models from data increases considerably with the …

The k-nearest neighbors classifier is a widely used classification method that has proven to
be very effective in supervised learning tasks. In this paper, a fuzzy rough set method for …

Multi-label classification has recently attracted research interest as a data mining task. Many
current applications in data mining address problems that have instances belonging to more …

Instance selection plays a critical role in enhancing the efficacy and efficiency of machine
learning tools when utilised for a data mining task. This study proposes a fixed instance …

Abstract Machine learning algorithms spend a lot of time processing data because they are
not fast enough to commit huge data sets. Instance selection algorithms especially aim to …

Although many more complex learning algorithms exist, k-nearest neighbor is still one of the
most successful classifiers in real-world applications. One of the ways of scaling up the k …