Bagging and Single Decision Tree Approaches to Dispersed Data

Abstract

The article is dedicated to the issue of classification based on independent data sources. A new approach proposed in the paper is a classification method for independent local decision tables that is based on the bagging method. For each local decision table, sub-tables are generated with the bagging method, based on which the decision trees are built. Such decision trees classify the test object, and a probability vector is defined over the decision classes for each local table. For each vector decision classes with the maximum value of the coordinates are selected and the final joint decision for all local tables is made by majority voting. The results were compared with the baseline method of generating one decision tree based on one local table. It cannot be clearly stated that more bootstrap replicates guarantee better classification quality. However, it was shown that the bagging classification trees produces more unambiguous results which are in many cases better than for the baseline method.