This practical guidebook describes the basic concepts, the mathematical developments, and the engineering methodologies for exploiting possibility theory for the computer-based design of an information fusion system where the goal is decision support for industries in smart ICT (information and communications technologies). This exploitation of possibility theory improves upon probability theory, complements Dempster-Shafer theory, and fills an important gap in this era of Big Data and Internet of Things. The book discusses fundamental possibilistic concepts: distribution, necessity measure, possibility measure, joint distribution, conditioning, distances, similarity measures, possibilistic decisions, fuzzy sets, fuzzy measures and integrals, and finally, the interrelated theories of uncertainty..uncertainty. These topics form an essential tour of the mathematical tools needed for the latter chapters of the book. These chapters present applications related to decision-making and pattern recognition schemes, and finally, a concluding chapter on the use of possibility theory in the overall challenging design of an information fusion system. This book will appeal to researchers and professionals in the field of information fusion and analytics, information and knowledge processing, smart ICT, and decision support systems.

Auteur
Basel Solaiman is a professor at IMT-Atlantique (École nationale supérieure Mines-Télécom Atlantique Bretagne-Pays de la Loire), France, where he heads the Department of Image and Information Processing. His research activities range from medical and underwater imaging, remote sensing, and knowledge mining. He holds a Ph.D. degree from Université de Rennes-I, France.

Éloi Bossé, is a researcher on decision support, fusion of information and analytics technologies (FIAT). He possesses a vast research experience in applying them to Defense and Security related problems. He is currently president of Expertise Parafuse Inc., a consultant firm on FIAT, associate researcher at IMT-Atlantique, France. He holds a Ph.D. degree from Université Laval, Québec City, Canada.

Contenu
Chapter1: Introduction to possibility theory.- Chapter2: Fundamental possibilistic concepts.- Chapter3: Joint Possibility Distributions and Conditioning.- Chapter4: Possibilistic Similarity Measures.- Chapter5: The interrelated uncertainty modeling theories.- Chapter6: Possibility integral.- Chapter7: Fusion operators and decision-making criteria in the framework of possibility theory.- Chapter8: Possibilistic concepts applied to soft pattern classification.- Chapter9: The use of possibility theory in the design of information fusion systems.