Simple and composed classifiers used for classification of experimental data

Abstract

Problems of classification has a great meaning at the handling of information. Statistical approaches, decision trees and approaches of artificial intelligence (sphere of neuron network) belong to standard methods of classification. This paper deals with simple classifiers k-nearest neighbors, Bayesian classifier, decision tree and also with composed classifiers - Bagging, Boosting and Stacked Generalization applied on experimental data sets.