Machine Learning
Machine Learning
Machine Learning
Maximum of entropy for credal sets
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data
International Journal of Approximate Reasoning
Bagging schemes on the presence of class noise in classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.