Incorporating Dynamic Uncertainties into a Fuzzy Classifier

Abstract

Dealing with classification problems in practice often has to cope with uncertain information, either in the training or in the operation phase or both. Modeling these uncertainties allows to enhance the robustness or performance of the classifier. In this paper we focus on the operation phase and present a general, but simple extension to rule based fuzzy classifier to do so. Therefor uncertain features are gradually and dimension wise faded out of the classification process. An artificial two-dimensional dataset is used to visualize the effectiveness of this approach. Investigations on three benchmark datasets shows the performance and gain in robustness.

Granular counting of uncertain data

“…statistical reasoning [15], supervised machine learning, where uncertainty can be either in the observed data [17] or in the target variables [18], or both [19].…”