<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fatourechi, M.</style></author><author><style face="normal" font="default" size="100%">Ward, R.K.</style></author><author><style face="normal" font="default" size="100%">Mason, S.G.</style></author><author><style face="normal" font="default" size="100%">Huggins, J.</style></author><author><style face="normal" font="default" size="100%">Schlogl, A.</style></author><author><style face="normal" font="default" size="100%">Birch, G.E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning and Applications, 2008. ICMLA '08. 7th International Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">classification application</style></keyword><keyword><style  face="normal" font="default" size="100%">classifier testing</style></keyword><keyword><style  face="normal" font="default" size="100%">evaluation metrics</style></keyword><keyword><style  face="normal" font="default" size="100%">imbalanced datasets</style></keyword><keyword><style  face="normal" font="default" size="100%">Kappa coefficient</style></keyword><keyword><style  face="normal" font="default" size="100%">model selection</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern classification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec.</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1109/ICMLA.2008.34</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">777 -782</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new framework is proposed for comparing evaluation metrics in classification applications with imbalanced datasets (i.e., the probability of one class vastly exceeds others). For model selection as well as testing the performance of a classifier, this framework finds the most suitable evaluation metric amongst a number of metrics. We apply this framework to compare two metrics: overall accuracy and Kappa coefficient. Simulation results demonstrate that Kappa coefficient is more suitable.</style></abstract></record></records></xml>