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Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Author

Listed:
  • Vassilis G. Kaburlasos

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Chris Lytridis

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Eleni Vrochidou

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Christos Bazinas

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • George A. Papakostas

    (HUMAIN-Lab, International Hellenic University (IHU), 65404 Kavala, Greece)

  • Anna Lekova

    (Institute of Robotics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria)

  • Omar Bouattane

    (SSDIA Lab, ENSET, University Hassan II of Casablanca, Mohammedia 28000, Morocco)

  • Mohamed Youssfi

    (SSDIA Lab, ENSET, University Hassan II of Casablanca, Mohammedia 28000, Morocco)

  • Takashi Hashimoto

    (School of Knowledge Science, Japan Advanced Institute of Science and Technology (JAIST), 1-1 Asahidai, Nomi 923-1292, Ishikawa, Japan)

Abstract

Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

Suggested Citation

  • Vassilis G. Kaburlasos & Chris Lytridis & Eleni Vrochidou & Christos Bazinas & George A. Papakostas & Anna Lekova & Omar Bouattane & Mohamed Youssfi & Takashi Hashimoto, 2021. "Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures," Mathematics, MDPI, vol. 9(22), pages 1-23, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2889-:d:678476
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    Citations

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    Cited by:

    1. Qiangqiang Chen & Linjie He & Yanan Diao & Kunbin Zhang & Guoru Zhao & Yumin Chen, 2022. "A Novel Neighborhood Granular Meanshift Clustering Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    2. Eleni Vrochidou & Viktoria Nikoleta Tsakalidou & Ioannis Kalathas & Theodoros Gkrimpizis & Theodore Pachidis & Vassilis G. Kaburlasos, 2022. "An Overview of End Effectors in Agricultural Robotic Harvesting Systems," Agriculture, MDPI, vol. 12(8), pages 1-35, August.

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