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

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 se...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vassilis G. Kaburlasos, Chris Lytridis, Eleni Vrochidou, Christos Bazinas, George A. Papakostas, Anna Lekova, Omar Bouattane, Mohamed Youssfi, Takashi Hashimoto
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Granular Computing human-robot interaction machine learning tree data structures Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/5ad0c4cea3e64891aaa57eaf870d799f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5ad0c4cea3e64891aaa57eaf870d799f
record_format	dspace
spelling	oai:doaj.org-article:5ad0c4cea3e64891aaa57eaf870d799f2021-11-25T18:16:53ZGranule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures10.3390/math92228892227-7390https://doaj.org/article/5ad0c4cea3e64891aaa57eaf870d799f2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2889https://doaj.org/toc/2227-7390Social 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.Vassilis G. KaburlasosChris LytridisEleni VrochidouChristos BazinasGeorge A. PapakostasAnna LekovaOmar BouattaneMohamed YoussfiTakashi HashimotoMDPI AGarticleGranular Computinghuman-robot interactionmachine learningtree data structuresMathematicsQA1-939ENMathematics, Vol 9, Iss 2889, p 2889 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Granular Computing human-robot interaction machine learning tree data structures Mathematics QA1-939
spellingShingle	Granular Computing human-robot interaction machine learning tree data structures Mathematics QA1-939 Vassilis G. Kaburlasos Chris Lytridis Eleni Vrochidou Christos Bazinas George A. Papakostas Anna Lekova Omar Bouattane Mohamed Youssfi Takashi Hashimoto Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
description	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.
format	article
author	Vassilis G. Kaburlasos Chris Lytridis Eleni Vrochidou Christos Bazinas George A. Papakostas Anna Lekova Omar Bouattane Mohamed Youssfi Takashi Hashimoto
author_facet	Vassilis G. Kaburlasos Chris Lytridis Eleni Vrochidou Christos Bazinas George A. Papakostas Anna Lekova Omar Bouattane Mohamed Youssfi Takashi Hashimoto
author_sort	Vassilis G. Kaburlasos
title	Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
title_short	Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
title_full	Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
title_fullStr	Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
title_full_unstemmed	Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures
title_sort	granule-based-classifier (gbc): a lattice computing scheme applied on tree data structures
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/5ad0c4cea3e64891aaa57eaf870d799f
work_keys_str_mv	AT vassilisgkaburlasos granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT chrislytridis granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT elenivrochidou granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT christosbazinas granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT georgeapapakostas granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT annalekova granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT omarbouattane granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT mohamedyoussfi granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures AT takashihashimoto granulebasedclassifiergbcalatticecomputingschemeappliedontreedatastructures
_version_	1718411382709288960

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

Ejemplares similares