Bayesian classifiers provide relatively good performance compared with other more complex algorithms. Misclassification ratio is very low for trained samples, but in the case of outliers the misclassification error may increase significantly. The usage of ‘summation hack’ method in Bayesian classification algorithm can reduce the misclassifications rate for untrained samples. The goal of this p...

In the usual setting of Machine Learning, classifiers are typically evaluated by estimating their error rate (or equivalently, the classification accuracy) on the test data. However, this makes sense only if all errors have equal (uniform) costs. When the costs of errors differ between each other, the classifiers should be evaluated by comparing the total costs of the errors. Classifiers are ty...

Performance accuracy of the Euclidean Distance Discriminant rule (EDDR) is studied in the high-dimensional asymptotic framework which allows the dimensionality to exceed sample size. Under mild assumptions on the traces of the covariance matrix, our new results provide the asymptotic distribution of the conditional misclassification error and the explicit expression for the consistent and asymp...