Classification model of poverty risk in the European Union

Abstract

Analysis of the at-risk-of-poverty dataset using WEKA machine learning software tool aims for mining the relationship in selected data from database Eurostat for efficient classification. We used eight classification algorithms for analyzing dataset. We used WEKA tools to search the best classification algorithm. We evaluated accuracy of classification algorithms using various accuracy measures like Kappa statistic, TP rate, FP rate, Precision, Recall, F-measure, ROC Area and PRC Area. The accuracy of the models was monitored by the number of instances classified correctly. In this paper we describe the values of the monitored indicators of the best algorithm J48.