This paper reviews methods for evaluating and analyzing the understandability of
classification models in the context of data mining. The motivation for this study is the fact …

Interpretability is often a major concern in machine learning. Although many authors agree
with this statement, interpretability is often tackled with intuitive arguments, distinct (yet …

The development of the industry phase from 4.0 into 5.0 is shifting the focus from technology-
driven to value-driven to empower and engage humans in the work environment with …

Recently, classifier ensemble methods are gaining more and more attention in the machine-
learning and data-mining communities. In most cases, the performance of an ensemble is …

En el campo de la inteligencia artificial, la clasificación de fenómenos mediante algoritmos
Machine y Deep Learning enfrentan problemas reales al utilizar datos con clases menos …

Although being a crucial question for the development of machine learning algorithms, there
is still no consensus on how to compare classifiers over multiple data sets with respect to …

This article makes the case for more objectivity in Machine Learning (ML) research. Any
research work that claims to hold benefits has to be scrutinized based on many parameters …

Abstract Operations Research (OR) and Artificial Intelligence (AI) disciplines have been
playing major roles on the design of new intelligent systems. Recently, different contributions …

The paper proposes a taxonomy for categorizing the main features of the supervised
learning classification problems and a notation for the identification of the supervised …

Abstract In 2014 European Food Safety Authority (EFSA) commissioned this evaluation of
the potential use of Machine Learning Techniques (MLTs) to provide insights for the …