Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …

Supervised machine learning methods typically require splitting data into multiple chunks for
training, validating, and finally testing classifiers. For finding the best parameters of a …

This study discusses the effects of class imbalance and training data size on the predictive
performance of classifiers. An empirical study was performed on ten classifiers arising from …

Using an ensemble of classifiers instead of a single classifier has been shown to improve
generalization performance in many machine learning problems [4, 16]. However, the extent …

Hyper-parameter optimization is a process to find suitable hyper-parameters for predictive
models. It typically incurs highly demanding computational costs due to the need of the time …

In this article we analyze the effect of class distribution on classifier learning. We begin by
describing the different ways in which class distribution affects learning and how it affects the …

Many real-world classification problems are significantly class-imbalanced to detriment of
the class of interest. The standard set of proper evaluation metrics is well-known but the …

Performance metrics in classification are fundamental in assessing the quality of learning
methods and learned models. However, many different measures have been defined in the …

Feature selection is often applied to high-dimensional data prior to classification learning.
Using the same training dataset in both selection and learning can result in so-called feature …

New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …