An application of Non-Parametric Predictive Inference on multi-class classification high-level-noise problems

This paper presents an application of the Non-parametric Predictive Inference model for multinomial data (NPIM) on multiclass classification noise tasks, i.e. classification tasks where the variable under study has 3 or more possible states or values; and the data sets have incorrect class labels in their training and/or test data sets. In an experimental study, we show that the combination or fusion of the information obtained from decision trees built using the NPIM in a Bagging scheme, can improve the process of classification in multi-class classification noise problems. Via a set of statistical tests, we compared this approach with other successful methods used in similar scheme, on a wide set of data sets. It must be remarked that the new approach has a notably performance, compared with the rest of models, when the level of noise is increased.

► An application on classification of a new non-parametric mathematical model based on imprecise probabilities is presented.
► The new method of classification obtains excellent results on data set with medium–high level of classification noise.
► We have compared the new method with others very successful methods via a set of test.
► The new method obtains a better Friedman’s rank than the rest ones when the level of noise is medium or high.