Document Type thesis Author Name Moraski, Ashley M. URN etd-050406-124140 Title Classification via distance profile nearest neighbors Degree MS Department Mathematical Sciences Advisors Jayson Wilbur, Advisor Keywords classification distance profile nearest neighbor Date of Presentation/Defense 2006-05-04 Availability unrestricted
Most classification rules can be expressed in terms of a distance (or dissimilarity) from the point to be classified to each of the candidate classes. For example, linear discriminant analysis classifies points into the class for which the (sample) Mahalanobis distance is smallest. However, dependence among these point-to-group distance measures is generally ignored. The primary goal of this project is to investigate the properties of a general non-parametric classification rule which takes this dependence structure into account. A review of classification procedures and applications is presented. The distance profile nearest-neighbor classification rule is defined. Properties of the rule are then explored via application to both real and simulated data and comparisons to other classification rules are discussed.
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