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Learning from Asymmetric Models and Matched Pairs


Abstract With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied variable selection of matched data sets and proposed a solution when there is non-linearity in the matched data. The resear... (more)
Created Date 2013
Contributor Koh, Derek (Author) / Runger, George (Advisor) / Wu, Tong (Committee member) / Pan, Rong (Committee member) / Cesta, John (Committee member) / Arizona State University (Publisher)
Subject Statistics / Computer science / Information science / asymmetry / feature selection / matched data / random forest / stratified data / support vector machine
Type Doctoral Dissertation
Extent 153 pages
Language English
Copyright
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Note Ph.D. Industrial Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis