Authors:
Francesco Cavrini
1
;
Lucia Rita Quitadamo
2
;
Luigi Bianchi
2
and
Giovanni Saggio
2
Affiliations:
1
University of Rome “La Sapienza”, Italy
;
2
University of Rome “Tor Vergata”, Italy
Keyword(s):
Brain-Computer Interface (BCI), Combination of Classifiers, Fuzzy Integral, Fuzzy Measure, Multi-Classifier Systems (MCS).
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Fuzzy Information Processing, Fusion, Text Mining
;
Fuzzy Systems
;
Mathematical Foundations: Fuzzy Set Theory and Fuzzy Logic
;
Pattern Recognition: Fuzzy Clustering and Classifiers
;
Soft Computing
Abstract:
In this paper we propose a framework for combination of classifiers using fuzzy measures and integrals that
aims at providing researchers and practitioners with a simple and structured approach to deal with two issues
that often arise in many pattern recognition applications: (i) the need for an automatic and user-specific selection
of the best performing classifier or, better, ensemble of classifiers, out of the available ones; (ii) the
need for uncertainty identification which should result in an abstention rather than an unreliable decision. We
evaluate the framework within the context of Brain-Computer Interface, a field in which abstention and intersubject
variability have a remarkable impact. Analysis of experimental data relative to five subjects shows that
the proposed system is able to answer such needs.