Entropy for Data-Driven Decision-Making Problems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 2807
Special Issue Editors
Interests: machine learning; sustainable development; entropy; decision making methods; operation management; sustainability
Special Issues, Collections and Topics in MDPI journals
Interests: operations research; optimization and decision analysis; multicriteria decision making; multiattribute decision making (MADM); decision support systems; civil engineering; energy; sustainable development; fuzzy sets theory; fuzzy multicriteria decision making; sustainability; management; game theory and economical computing knowledge management
Special Issues, Collections and Topics in MDPI journals
Interests: multi-criteria; fuzzy set; soft computing; renewable energy; sustainability; circular economy; technology assessment; hypersoft sets; sustainable development goals
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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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Keywords
- entropy
- data-driven decision-making
- machine learning
- deep learning
- predictive modeling
- decision making
- complex systems
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