A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach

  • Moacir P. PontiJr.
  • João Paulo Papa
  • Alexandre L. M. Levada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


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.


Majority Vote Markov Random Field Star Graph Markov Random Field Model Decision Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Moacir P. PontiJr.
    • 1
  • João Paulo Papa
    • 2
  • Alexandre L. M. Levada
    • 3
  1. 1.Institute of Mathematical and Computer SciencesUniversity of São Paulo (ICMC/USP)São CarlosBrazil
  2. 2.Department of ComputingUNESP — Univ Estadual PaulistaBauruBrazil
  3. 3.Computing DepartmentFederal University of São Carlos (DC/UFSCar)São CarlosBrazil

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