Okwonu, Friday Zinzendoff
(2013)
Several Robust Techniques In Two-Groups
Unbiased Linear Classification.
PhD thesis, Universiti Sains Malaysia.
Abstract
The fundamental difficulty in classification problem is how to assign an
observation accurately to the group it belongs. This thesis is written based on the
limitations and weaknesses of the Fisher linear classification analysis and its robust
version based on the minimum covariance determinant estimator. The Fisher’s
procedure is not robust while the robust version depends upon information obtained
from the half set. This study develops several techniques to address the weaknesses
of the two methods. They are: M linear classification rule, filter linear classification
rule, weighted linear classification rule and linear combination linear classification
rule. These procedures are developed in such a way that the influential observations
are modeled alongside the regular observations. The robustness and stability of these
techniques depends on the separation parameters. Contamination models and control
variables were used to investigate the classification performance of these linear
classification rules. Classification difference was used to compare the classification
performance of the proposed techniques over the Fisher linear classification analysis
and the Fisher linear classification analysis based on the minimum covariance
determinant procedures. The mean probability of correct classification for each
procedure was used to compare the mean of the optimal probability of correct
classification obtained from the uncontaminated data set in order to ascertain
robustness, breakdown and admissibility of these techniques.
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