On developing Sugeno fuzzy measure densities in problems of face recognition

Fuzzy measures and Choquet integral are efficient aggregation operators utilised intensively in decision-making theory. To produce sound classification results based on a family of classifiers, the parameters of the fuzzy measure (especially, so-called fuzzy densities) have to be determined. In this study, we propose a method based on particle swarm optimisation (PSO) and discuss in detail a new concept of a so-called positive and negative optimisation to fully utilise specific properties of classifiers to carry out efficient classification. A suite of experiments is conducted to illustrate this approach and discuss its scope of applicability.