Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making.

The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization.

Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains. Additionally, it extends its reach to practitioners in data analytics, business intelligence, risk management, and strategic decision-making, offering pragmatic insights and methodologies. Graduate students in data science and decision analysis can also benefit from the book's potential as a textbook or supplementary reading material. By covering an array of topics, from big data analytics to deep learning models and game theory, this book positions itself as an indispensable resource, guiding readers towards mastering the fusion of big data and decision-making in an era defined by complexity and uncertainty.

Coverage:

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

Big Data Analytics
Decision-Making Under Uncertainty
Deep Learning Models
Formal Concept Analysis
Fuzzy Decision-Making
Game Theory
Granular Computing
Intelligent Control Systems
Multi-Granularity Analysis
Natural Language Processing
Neural Network Models
Rough Set Theory
Uncertainty Modeling

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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