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Synopsis
This book explores model selection using decision trees based on discretized data, and the accompanying implications this has for decision-makers. Model selection is a non-trivial exercise with a large impact on decision-making. An information criterion for selecting between competing decision tree models is presented along with a method for quantifying the opportunity costs of choosing a specific decision tree. After the initial information criterion development a real world example from an American insurance company call center is presented. The example includes the development of a program to automate data discretization, decision tree creation, and decision tree selection. A decision tree is chosen and critically analyzed from a managerial decision-making point of view. Actual call center performance data is used as input data and results are identified and presented in a way that is advantageous to managers.
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About the Author
Eric Kyper is an assistant professor of management information systems at Lynchburg College. He graduated with a Ph.D. in MIS from the University of Rhode Island. His research focuses primarily on quantitative analysis and data mining. Dr. Lloyd received his B.S.and M.S.in Information Systems from Virginia Commonwealth University. He was awarded a Information System Ph.D. from Kent State University in 1996. Since that time he has been a professor and is currently at the University of Rhode Island.
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- PublisherVDM Verlag
- Publication date2008
- ISBN 10 3836489856
- ISBN 13 9783836489850
- BindingPaperback
- LanguageEnglish
- Number of pages52
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Improved Decision-making in Data Mining: A Heuristic Rule Induction Approach to Decision Tree Creation and Model Selection
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