Entropy for Data-Driven Decision-Making Problems

Dear Colleagues,

Over the last few years, a need for data-driven decision-making modeling has arisen to deliver real-time solutions to problems by integrating models from the rapidly developing fields of machine learning, deep learning, and entropy. Machine learning is an approach for data analysis that constructs the analytical model by giving computer systems the ability to “learn.” Machine learning and deep learning models are based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. The concept of entropy was originally developed in the field of physics, but it is clear that entropy is deeply related to machine learning and deep learning. Furthermore, besides applications in machine learning, entropy is a general measure commonly used for the qualitative analysis of complex systems. In this regard, entropy is a powerful descriptive method that presents an operational and theoretical framework to attain both qualitative and quantitative descriptions of the intrinsic properties of machine learning and deep learning theories. Therefore, to understand the importance of entropy concepts in data-driven decision-making problems using machine learning and deep learning, in this Special Issue, we are interested in providing state‐of‐the‐art literature on entropy concepts and establishing a reliable connection between data-driven decision-making problems using machine learning and deep learning contexts.

Dr. Abbas Mardani
Prof. Dr. Edmundas Kazimieras Zavadskas
Prof. Dr. Fausto Cavallaro
Guest Editors

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