TITLE:
Robust Classification through a Nonparametric Kernel Discriminant Analysis
AUTHORS:
Macdonald G. Obudho, George O. Orwa, Romanus O. Otieno, Festus A. Were
KEYWORDS:
Discriminant Analysis, Kernel Discriminant, Nonparametric
JOURNAL NAME:
Open Journal of Statistics,
Vol.12 No.4,
August
11,
2022
ABSTRACT: The problem of
classification in situations where the assumption of normality in the data is
violated, and there are non-linear clustered structures in the dataset is
addressed. A robust nonparametric kernel discriminant classification function,
which is able to address this challenge, has been developed and the
misclassification rates computed for various bandwidth matrices. A comparison
with existing parametric classification functions such as the linear
discriminant and quadratic discriminant is conducted to evaluate the
performance of this classification function using simulated datasets. The
results presented in this paper show good performance in terms of misclassification
rates for the kernel discriminant classifier when the correct bandwidth is selected
as compared to other identified existing classifiers. In this regard, the study
recommends the use of the proposed kernel discriminant classification rule when
one wishes to classify units into one of several categories or population
groups where parametric classifiers might not be applicable.