THE HYBRID CLASSIFICATION USING EMPIRICAL BAYES AND NEAREST NEIGHBOR
N. Deetae, Saowanit Sukparungsee, Yupaporn Areepong, Katechan Jampachaisri · International Journal of Pure and Apllied Mathematics · 2012
Classification is emphasized on allocating new observations in the test set of sample to labeled classes based on constructed rule from the training set. With the hybrid of several classification techniques has been developed and mostly exhibited results superior to a single classification technique. The aim of this study is to develop a new classification technique using Empirical Bayes in combination with Nearest Neighbor (EBNN) in the case of unknown mean and known variance. The realization of estimated hyper-parameters obtained from Empirical Bayes (EB) were adjusted using Nearest Neighbor method (NN), providing improved prediction of class membership when compared to that using single method. Data employed in this study are generated, consisting of training set and test set with the sample sizes 100, 200 and 500 for the binary classification. The results indicated EBNN method exhibited an improved performance over EB method in all situations under study. AMS Subject Classification: 62H30, 62F15, 62C12