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Informationen zum Autor Ting Yu , Ph.D., is an honorary research fellow in the Integrated Sustainability Analysis Group at the University of Sydney. He is also a transport modeler for the Transport for NSW. His research interests include machine learning, data mining, parallel computing, applied economics, and sustainability analysis. He earned a Ph.D. in computing science from the University of Technology, Sydney. Nitesh Chawla , Ph.D., is an associate professor in the Department of Computer Science and Engineering, director of the Interdisciplinary Center for Network Science and Applications, and director of the Data Inference Analysis and Learning Lab at the University of Notre Dame. A recipient of multiple awards for research and teaching, Dr. Chawla is chair of the IEEE Computational Intelligence Society Data Mining Technical Committee and associate editor of IEEE Transactions on Systems, Man and Cybernetics (Part B) and Pattern Recognition Letters . His research focuses on machine learning, data mining, and network science. Simeon Simoff , Ph.D., is dean of the School of Computing, Engineering and Mathematics at the University of Western Sydney. He is also a founding director and fellow of the Institute of Analytics Professionals of Australia. He serves on the American Society of Civil Engineering Technical Committees on Data and Information Management and on Intelligent Computing and is an editor of the Australian Computer Society’s Conferences in Research and Practice in Information Technology . Klappentext Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. This volume presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. Zusammenfassung Going beyond performing simple analyses! researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments! including economic! environmental! and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications! it illustrates important data analysis methods! including mathematical optimization! machine learning! signal processing! and temporal and spatial analysis! for quantifying and describing sustainable development problems.With a focus on integrated sustainability analysis! the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas! revealing that aggregation errors have a high concentration over specific regions and sectors.The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available! providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handle...
