Kernels on fuzzy sets: an overview
This work addresses uncertainty in data for machine learning and data science tasks, but it appears incremental as it extends kernel methods to fuzzy sets without claiming major breakthroughs.
The paper tackles the problem of measuring similarity for fuzzy sets by introducing kernels on fuzzy sets as a similarity measure for membership functions, defining classes such as cross product, intersection, non-singleton, and distance-based kernels.
This paper introduces the concept of kernels on fuzzy sets as a similarity measure for $[0,1]$-valued functions, a.k.a. \emph{membership functions of fuzzy sets}. We defined the following classes of kernels: the cross product, the intersection, the non-singleton and the distance-based kernels on fuzzy sets. Applicability of those kernels are on machine learning and data science tasks where uncertainty in data has an ontic or epistemistic interpretation.