A kernel-based framework for learning graded relations from data

TL;DR

This paper introduces a kernel-based framework that effectively learns both crisp and graded relations between data objects, unifying various approaches and applicable to real-world problems like bioinformatics and social networks.

Contribution

It extends existing methods by incorporating graded relations and unifies different types of relations, bridging fuzzy set theory and machine learning.

Findings

Framework models both crisp and graded relations

Demonstrated effectiveness on synthetic and real data

Unifies multiple relation learning approaches

Abstract

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data.