Flexible Classification Techniques with Biomedical Applications Public Deposited

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Last Modified
  • March 19, 2019
Creator
  • Zhang, Chong
    • Affiliation: College of Arts and Sciences, Department of Statistics and Operations Research
Abstract
  • Classification problems are prevalent in many scientific disciplines, especially in biomedical research. Recently, margin based classifiers have become increasingly popular, partly due to their ability in handling large scale problems with fast computational speed and desirable theoretical properties. Despite the success of margin based classifiers, many challenges remain. For example, in practical problems, it can be desirable to estimate the class conditional probability accurately. For high dimensional classification data, penalized margin based classifiers are commonly used. However, when estimating the class conditional probability, the shrinkage effect from the penalty term in the corresponding optimization is often ignored. This effect can lead to large bias in estimation of the class conditional probability. Another important issue on classification is the comparison between soft and hard classifiers for multicategory problems. Moreover, regular multicategory margin based classifiers can suffer from inefficiency by using too many classification functions. In this dissertation, we propose several new classification techniques to overcome the challenges mentioned above. Comprehensive numerical and theoretical studies are presented to demonstrate the usefulness of our new proposed methodologies.
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  • In Copyright
Advisor
  • Marron, James Stephen
  • Zeng, Donglin
  • Nobel, Andrew
  • Liu, Yufeng
  • Zhang, Kai
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2014
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  • Chapel Hill, NC
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  • This item is restricted from public view for 1 year after publication.
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