In an era defined by the deluge of data, navigating the complexities of decision-making under conditions of uncertainty has emerged as a formidable challenge for scholars and practitioners alike. The sheer volume and velocity of information inundating decision-makers often leads to paralysis or misguided choices, amplifying the risks inherent in uncertain environments. Granular Computing and Big Data Advancements provides insights and solutions in this challenging landscape.

This comprehensive publication addresses the fundamental dilemma decision-makers face: how to harness the power of big data amidst uncertainty to make informed choices that drive success. By delving into the intricacies of big data analytics, uncertainty modeling, and advanced quantification techniques, the book equips scholars with the necessary tools to extract meaningful insights from vast datasets and confidently navigate uncertainty. Through real-world case studies spanning diverse domains such as finance, healthcare, and marketing, readers gain invaluable practical insights into applying these methodologies in their contexts.

The impact of Granular Computing and Big Data Advancements reverberates across the research community, offering a cohesive resource that bridges the gap between theory and practice. With its interdisciplinary approach and emphasis on innovation, the book fosters collaboration and empowers scholars to tackle complex challenges head-on. Whether researchers seek novel methodologies, practitioners aim to enhance decision-making processes, or students embark on their academic journey, this publication serves as a cornerstone in the quest for effective decision-making amidst the uncertainties of the modern world.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Bayesian Inference Graphical Models
Customer Behavior Analysis
Data Mining Algorithms for Big Data Analytics
Decision Rules
Decision Trees
Emerging Trends in Big Data Analytics and Decision-Making
Ethical and Societal Implications of Big Data-Driven Decision-Making
Evolutionary Algorithms for Decision-Making
Fairness and Bias in Decision-Making Algorithms
Financial Decision-Making Under Uncertainty
Foundations of Decision Theory
Fuzzy Logic and Fuzzy Sets in Decision-Making
Healthcare Analytics and Medical Decision Support Systems
Leveraging Big Data for Decision-Making
Linear Programming for Decision Optimization
Machine Learning Algorithms for Big Data Analytics
Metaheuristics for Decision-Making
Nonlinear Programming for Decision Optimization
Probabilistic Graphical Models
Regulations Compliance

Table of Contents

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Editor/Author Biographies

Chao Zhang , professor of Institute of Intelligent Information Processing, Shanxi University, his main study interests include data mining, granular computing and intelligent decision making. In recent years, he has published more than 80 papers including IEEE Transactions on Computational Social Systems, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Consumer Electronics, Information Sciences, Information Fusion, International Journal of Approximate Reasoning, ACM Transactions on Asian and Low-Resource Language Information Processing, Computers in Industry, Applied Mathematical Modelling. Among them, one paper has been selected as “ESI highly cited paper”. He has published 3 academic monographs in national publishers. He has published 2 national invention patents. He has been awarded the first prize of Outstanding Achievements in Scientific Research in Institutions of Higher Learning in Shanxi Province, the second prize of Outstanding Achievement Award in Social Sciences in Shanxi Province, two Excellent Academic Paper Awards in Taiyuan City, ACM Excellent Doctoral Dissertation Award in Taiyuan Chapter, the best student paper award in CGCKD.

Wentao Li received the Ph.D. degree from the Department of Mathematics, Harbin Institute of Technology, Harbin, China, in 2019, and the M.Sc. degree from the School of Mathematics and Statistics, Chongqing University of Technology, Chongqing, China, in 2015. From 2016 to 2018, he was a joint Ph. D student with the University of Alberta, Edmonton, AB, Canada. He is currently an Associate Professor with the College of Artificial Intelligence, Southwest University, Chongqing, China. His current research interests include artificial intelligence, feature selection, and granular computing. In recent years, He has published over 30 articles including IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Emerging Topics in Computational Intelligence, Fuzzy Sets and Systems, Information Sciences, Artificial Intelligence Review, and many others. Among them, 3 papers have been selected as “ESI hot paper” and “ESI highly cited paper”. He serves on editorial board of several international journals, and successfully held several Special Issues in journals of International Journal of Fuzzy Systems, Wireless Communications and Mobile Computing, Intelligent Automation & Soft Computing, and Frontiers in Neurorobotics. He also served as the reviewer of many journals, such as IEEE TFS, IEEE TNNLS, IEEE TCYB, IEEE TKDE, INS, KBS, AIRE, and granted the certificate of “Outstanding Reviewer” for Knowledge-Based Systems in 2018.

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