Machine Learning and Probabilistic Graphical Models for Decision Support Systems

We are in the midst of rapid development and era of use of powerful applications of advanced technologies, leading to the 4th industrial revolution. The wide use of cyber-physical systems and the Internet of Things lead to the era of Big Data. A decision support system (DSS) is an information system that analyses data from organizations and presents it so that managers can make decisions more easily. In the era of Big Data, DSS has become vital for organizations. Machine learning is a powerful form of Artificial Intelligence that can be useful to process and analyze Big Data. Machine learning has the potential to advance DSS with a combination of data dictated and human-driven analytics. DSS applications can be used in a vast array of diverse fields, such as making operational decisions, medical diagnosis, and predictive maintenance.

While substantial research has been conducted in the development and application of DSS, there are no reference publications presenting systematically and in depth, the application of Machine Learning to develop the DSS in the context of the process with uncertainty. This book presents recent advancements in research, new methods and techniques, and applications in DSS with Machine Learning and Probabilistic Graphical Models which are very powerful techniques to extract knowledge from big data effectively and interpret decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multi-criteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. The book aims to stimulate scientific exchange, ideas, and experiences in the field of DSS applications. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their use in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.