A main idea underlying the models is to consider a set of probability distributions on training examples produced by the imprecise probability models such as ...

A framework for constructing robust classification models is proposed in the paper. An assumption about importance of one of the classes in comparison with ...

We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions.

Abstract. In real-world environments it is usually difficult to specify target operating conditions precisely. This un- certainty makes building robust ...

The minimax strategy is used for building weak classifiers at each iteration. The local sets of weights are constructed by means of imprecise statistical models ...

Dec 13, 2022 · By robust, we mean that we consider imprecise models that may abstain to classify or to compare two classes when information is insufficient.

A framework for constructing robust one-class classification models based on Walley's imprecise extensions of contaminated models which produce a set of ...

In this paper we suggest a new forward search algorithm for clustering multivariate categorical observations. Classification based on categorical information ...

A robust classifier is optimal under all possible conditions. In principle, classification brittleness could be overcome by saving all possible classifiers. ( ...

Missing: Classifiers Classes.