An interval-valued fuzzy classifier based on an uncertainty-aware similarity measure

Authors:

• Anna Stachowiak,
• Patryk Żywica,
• Krzysztof Dyczkowski,
• Andrzej Wójtowicz

Abstract

In this paper we propose a new method for classifying uncertain data, modeled as interval-valued fuzzy sets. We develop the notion of an interval-valued prototype-based fuzzy classifier, with the idea of preserving full information including the uncertainty factor about data during the classification process. To this end, the classifier was based on the uncertainty-aware similarity measure, a new concept which we introduce and give an axiomatic definition for. Moreover, an algorithm for determining such a similarity value is proposed, and an application to supporting medical diagnosis is described.