Emerging Methods in Predictive Analytics: Risk Management and Decision-Making
- 10h 41m
- William H. Hsu (ed)
- IGI Global
- 2014
Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made.
Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.
About the Editor
William H. Hsu is an associate professor of Computing and Information Sciences at Kansas State University. He received a B.S. in Mathematical Sciences and Computer Science and an M.S.Eng. in Computer Science from Johns Hopkins University in 1993, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1998. His dissertation explored the optimization of inductive bias in supervised machine learning for predictive analytics. At the National Center for Supercomputing Applications (NCSA) he was a co-recipient of an Industrial Grand Challenge Award for visual analytics of text corpora. His research interests include machine learning, probabilistic reasoning, and information visualization, with applications to cybersecurity, education, digital humanities, geoinformatics, and biomedical informatics. Published applications of his research include structured information extraction; spatiotemporal event detection for veterinary epidemiology, crime mapping, and opinion mining; analysis of heterogeneous information networks. Current work in his lab deals with: data mining and visualization in education research; graphical models of probability and utility for information security; developing domain-adaptive models of large natural language corpora and social media for text mining, link mining, sentiment analysis, and recommender systems. Dr. Hsu has over 50 refereed publications in conferences, journals, and books, plus over 35 additional publications.
In this Book
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Prescriptive Analytics Using Synthetic Information
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A Recommendation System for Scientific Papers Through Bayesian Nonparametric Hybrid Filtering
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Applications in Predictive Analytics: Ubiquitous Management Methodology for Predictive Maintenance in Medical Devices
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Application of Spatial and Temporal Predictive Analysis for Energy Network Optimization
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Achieving RF Jamming with DSA-Enabled Cognitive Radio Swarms: A Guide to Trends, Technologies, and Approaches in the Information Sciences
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Non-Parametric Stakeholder Discovery: A Process for Mitigating Risk Governance Deficits Through Open-Ended Protocols
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Predictive Network Defense: Using Machine Learning Algorithms to Protect an Intranet from Cyberattack
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Computing Skills in Forecasting for Liquidity Risk Management in the Indian Banking Industry
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Continuous-Time Infinite Dynamic Topic Models: The Dim Sum Process for Simultaneous Topic Enumeration and Formation
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Predictive Analytics in Digital Signal Processing: A Convolutive Model for Polyphonic Instrument Identification and Pitch Detection Using Combined Classification
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Biometric Authentication: Verifying a User's Identity Using a Frequentist Probability Model of Keystroke Intervals
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Exploration of Soft Computing Approaches in Itemset Mining
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Predictive Analytics of Social Networks: A Survey of Tasks and Techniques
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Money Supply: Predictive Analytics in India
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Overview of Predictive Modeling Approaches in Health Care Data Mining
