Title: On developing Sugeno fuzzy measure densities in problems of face recognition

Authors: Paweł Karczmarek; Adam Kiersztyn; Witold Pedrycz

Addresses: Institute of Mathematics and Computer Science, The John Paul II Catholic University of Lublin, ul.Konstantynów 1H, 20-708, Lublin, Poland ' Institute of Mathematics and Computer Science, The John Paul II Catholic University of Lublin, ul.Konstantynów 1H, 20-708, Lublin, Poland ' Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

Abstract: 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.

Keywords: fuzzy measure; Choquet integral; particle swarm optimisation; PSO; face recognition.

DOI: 10.1504/IJMISSP.2017.088185

International Journal of Machine Intelligence and Sensory Signal Processing, 2017 Vol.2 No.1, pp.80 - 96

Received: 15 May 2017
Accepted: 24 Jul 2017
Published online: 27 Nov 2017 *

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