Aggregating α-planes for Type-2 fuzzy set matching

This paper proposes a novel way of matching general type-2 fuzzy sets using a sequence-based approach. General sequences are defined as an ordered list of objects, which are called events. In our contribution, an event of the sequenced type-2 fuzzy set is defined as the footprint of uncertainty of a specific α-plane. Suited matching algorithms for generalized sequences can be applied to this new interpretation, enabling the computation of the overall dissimilarity value between input type-2 fuzzy sets. The evaluation of the proposed matching method is carried out in the setting of classification, by defining datasets of general type-2 fuzzy sets conceived as labeled patterns. Results show that the methodology is robust, accurate, and computationally acceptable.