Big Data applications are emerging during the last years, and researchers from many
disciplines are aware of the high advantages related to the knowledge extraction from this …

Current benchmark reports of classification algorithms generally concern common classifiers
and their variants but do not include many algorithms that have been introduced in recent …

Background The selection, development, or comparison of machine learning methods in
data mining can be a difficult task based on the target problem and goals of a particular …

Abstract The k-Nearest Neighbor algorithm (kNN) is an algorithm that is very simple to
understand for classification or regression. It is also a lazy algorithm that does not use the …

Abstract The k-Nearest Neighbors classifier is a simple yet effective widely renowned
method in data mining. The actual application of this model in the big data domain is not …

Historical data sets for fault stage diagnosis in industrial machines are often imbalanced and
consist of multiple categories or classes. Learning discriminative models from such data sets …

This paper introduces the 3rd major release of the KEEL Software. KEEL is an open source
Java framework (GPLv3 license) that provides a number of modules to perform a wide …

There have been many attempts to classify imbalanced data, since this classification is
critical in a wide variety of applications related to the detection of anomalies, failures, and …

In machine learning, discretization and feature selection (FS) are important techniques for
preprocessing data to improve the performance of an algorithm on high-dimensional data …

The evaluation of artificial intelligence systems and components is crucial for the progress of
the discipline. In this paper we describe and critically assess the different ways AI systems …