Comparison between Fisherian and Bayesian Approach to Classification Using two Groups
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
College of Natural and Applied Science, University of Porthacourt, Nigeria
Abstract
Two approaches to discriminant analysis procedure are examined and compared based on
their misclassification error rate. The Fisher's approach tends to find a linear combination of
the variables which maximize the ratio of the between group sum of squares to that of the
within group sum of squares in achieving a good separation. On the other hand, the Bayesian
approach assigns an observed unit to a group with the greatest posterior probability.
Fisher's linear discriminant analysis though is the most widely used method of classification
because of its simplicity, and optimality properties is normally used for two group cases.
However, Bayesian approach is found to be better than Fisher's approach because of its low
misclassification error rate.
Description
Keywords
Variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification
Citation
Scientia Africana