Research and industry increasingly make use of large amounts of data to guide decision-
making. To do this, however, data needs to be analyzed in typically nontrivial refinement …

Metalearning attracted considerable interest in the machine learning community in the last
years. Yet, some disagreement remains on what does or what does not constitute a …

This paper introduces a software tool named KEEL which is a software tool to assess
evolutionary algorithms for Data Mining problems of various kinds including as regression …

Metalearning is the study of principled methods that exploit metaknowledge to obtain
efficient models and solutions by adapting machine learning and data mining processes …

KDD is a complex and demanding task. While a large number of methods has been
established for numerous problems, many challenges remain to be solved. New tasks …

With almost everything now online, organizations look at the Big Data collected to gain
insights for improving their services. In the analytics process, derivation of such insights …

A data mining (DM) process involves multiple stages. A simple, but typical, process might
include preprocessing data, applying a data mining algorithm, and postprocessing the …

Thousands of machine learning research papers contain extensive experimental
comparisons. However, the details of those experiments are often lost after publication …

Current data mining tools are characterized by a plethora of algorithms but a lack of
guidelines to select the right method according to the nature of the problem under analysis …

Abstract Recently, advances in Information Technologies (social networks, mobile
applications, Internet of Things, etc.) generate a deluge of digital data; but to convert these …