An Overview of the Classification Problem in Unbalanced Datasets Using the Statistical Construction of European Community Economic Activities

Abstract

The use of classical classifiers in unbalanced and multi-class data sets has always been a problem. In this study, a text mining work has been applied with well-known classifiers on the definitions of Statistical Construction of Economic Activities (NACE) codes in the European Community. In the study, first of all, the application was made on the unbalanced structure of the original data, then the performance measurement was performed by retesting the result data by making it balanced by weighting on a class basis. Common classifiers such as Decision Trees, Naiv Bayes, Support Vector Machines, Diametric Based Functions and Random Forest algorithms were used in the tests. The study showed us that as a result of data balancing of Decision Trees, the F-score value increased from 17.43% to 92%, giving the best performance.