Artificial Intelligence Research
International Peer-reviewed and Open Access Journal for the Artificial Intelligence Specialists

Ordinal classifiers have become quite popular in recent years. However, no one has systematically tested yet how sensitive theyare to noise. This research investigates for the first time the effect of non-monotone noise on the accuracy related rankings of tenclassifiers in a controlled manner. The findings of this experiment are reported here. They clearly show that some models aremore sensitive than others to non-monotone noise. Some classifiers which ranked higher in absence of noise performed poorlywhen the noise level increased even modestly. Others, which ranked relatively low in noiseless datasets, ranked much betterwhen the noise levels increased. Two classifiers which assure monotone classifications became practically useless at relativelylow levels of noise, while other classifiers’ accuracies deteriorated at a much slower pace. Three alternative accuracy-relatedmeasures were used: Accuracy, Kappa and the Gini Index, and all were subjected to statistical tests. The lesson to be learnedfrom this experiment is that it is very important to measure and report, among other things, the levels of noise which are presentin datasets used for the evaluation of classification models.