Class noise refers to the incorrect labeling of examples in classification datasets and causes the deterioration of the performance of the classifiers. This contribution studies the differences between the behavior of Multiple Classifier Systems and their components when they are trained with data suffering from class noise. The results obtained show that combining classifiers usually help when dealing with classifica- tion problems suffering from class noise. In general, the combined models are more accurate than the component classifiers, and the robustness is comparable or slightly better.
['José A. Sáez', 'Mikel Galar', 'Julián Luengo', 'Francisco Herrera']
An Experimental Case of Study on the Behavior of Multiple Classifier Systems with Class Noise Datasets