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The Performance of Classification Using Empirical Bayes in Heavy-tailed Distribution
Abstract
The objective of this study is to develop a classification technique in normal and heavy-tailed asymmetric distributions, using Empirical Bayes method (EB) with conjugate priors, normal distributions with known mean but unknown variance. The results are compared to Classical and K-nearest neighbour method (K-NN). In each situation under study, the average percentage of correct classification is considered. For all sample sizes, the study shows that EB method outperforms Classical and K-NN method in normal distribution whereas K-NN method outperforms EB and Classical method in heavy-tailed asymmetric distribution. In addition, the average percentages of correctly classified data in K-NN tend to be higher as the number of neighbourhoods (k) increases.
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