A markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

Authors:
Moacir P. Ponti;João Paulo Papa;Alexandre L. M. Levada
Affiliations:
Institute of Mathematical and Computer Sciences, University of São Paulo (ICMC/USP), São Carlos, SP, Brazil;Department of Computing, UNESP — Univ Estadual Paulista, Bauru, SP, Brazil;Computing Department, Federal University of São Carlos (DC/UFSCar), São Carlos, SP, Brazil
Venue:
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2011

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Abstract

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems.