Emerging Methods in Predictive Analytics: Risk Management and Decision-Making

Emerging Methods in Predictive Analytics: Risk Management and Decision-Making

William H. Hsu (Kansas State University, USA)
Indexed In: SCOPUS
Release Date: January, 2014|Copyright: © 2014 |Pages: 425
DOI: 10.4018/978-1-4666-5063-3
ISBN13: 9781466650633|ISBN10: 146665063X|EISBN13: 9781466650640
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Description & Coverage
Description:

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.

Coverage:

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

  • Data Mining for Predictive Analytics
  • Data Visualization
  • Information Value
  • Market Manipulation
  • Pattern Analysis
  • Predictive Analytics Applications
  • Verifying Predictive Analytics Systems
Reviews and Testimonials

Contributors from the computer and information sciences survey recent developments in predictive analytics, including methods for forecasting, modeling, and understanding time series, detecting anomalies and emerging issues, inferring causality over time, presenting identified patterns interactively, and validating analytical models using real-world historical data. Among the topics are ubiquitous management methodology for predictive maintenance in medical devices, spatial and temporal predicting analysis for energy network optimization, using machine learning algorithms to protect an intranet from cyberattack, verifying a user's identity using a frequentist probability model of keystroke intervals, and predicting analytics of money supply in India.

– ProtoView Book Abstracts (formerly Book News, Inc.)
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Editor/Author Biographies
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.
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