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A Kernel-Based Optimum-Path Forest Classifier

  • Luis C. S. Afonso
  • Danillo R. Pereira
  • João P. Papa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The modeling of real-world problems as graphs along with the problem of non-linear distributions comes up with the idea of applying kernel functions in feature spaces. Roughly speaking, the idea is to seek for well-behaved samples in higher dimensional spaces, where the assumption of linearly separable samples is stronger. In this matter, this paper proposes a kernel-based Optimum-Path Forest (OPF) classifier by incorporating kernel functions in both training and classification steps. The proposed technique was evaluated over a benchmark comprised of 11 datasets, whose results outperformed the well-known Support Vector Machines and the standard OPF classifier for some situations.

Keywords

Optimum-Path Forest Kernel Support Vector Machines 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ComputingUFSCar - Federal University of São CarlosSão CarlosBrazil
  2. 2.University of Western São PauloPresidente PrudenteBrazil
  3. 3.School of SciencesUNESP - São Paulo State UniversityBauruBrazil

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