Ensemble Algorithms and/or Explainability

Dear Colleagues,

We invite you to submit your latest research in the area of “Ensemble Algorithms and/or Explainability” to this Special Issue.

During the last several decades, in the area of machine learning and data mining, ensemble methods have constituted a state-of-the-art choice for the development of powerful and robust prediction models. These models exploit the individual predictions of a variety of constituent learning algorithms to obtain better prediction performance, which was proved both theoretically and experimentally. Thus, many ensemble learning algorithms have been proposed in the literature and found their application in various real-world problems ranging from face and emotion recognition through text classification and medical diagnosis to financial forecasting, to mention only a few.

Recently, the European Union General Data Protection Regulation (GDPR) demanded a “right to explanation” for decisions performed by automated and artificial intelligent algorithmic systems. This demand, combined with the need to be able to interpret or explain and justify the decisions/predictions of a classifier or ensemble which has already been recognized by many researchers, led to the development of “interpretable/explainable machine learning and artificial intelligence” which has gained great attention from the scientific community.

Given that ensembles and deep-learning models produce more accurate predictions, we need to develop new methods and algorithms in order to create explainable ML and AI models which are nearly as accurate as the non-explainable ones.

The aim of this Special Issue is to present the recent advances related to all kinds of ensemble learning algorithms and methodologies and investigate the impact of their application in a diversity of real-world problems. At the same time, the need to research the explainability issues involved in theory and practice has become of paramount importance for all kinds of daily and industrial applications.

Prof. Dr. Panagiotis Pintelas
Dr. Ioannis E. Livieris
Guest Editors

Manuscript Submission Information

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