Visual approach to support analysis of optimum-path forest classifier

dc.contributor.authorEler, Danilo Medeiros [UNESP]
dc.contributor.authorBatista, Matheus Prachedes [UNESP]
dc.contributor.authorGarcia, Rogério Eduardo [UNESP]
dc.contributor.authorPereira, Danillo Roberto
dc.contributor.authorMarcilio, Wilson Estecio [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Do Oeste Paulista (UNOESTE)
dc.date.accessioned2020-12-12T02:32:20Z
dc.date.available2020-12-12T02:32:20Z
dc.date.issued2019-10-01
dc.description.abstractOptimum-path forest (OPF) is a graph based classifier in which the training process computes optimum-path trees rooted by prototype instances. Thus, one or more optimum-path trees represent each class and the testing process is based on identifying which optimum-path tree would contain a test sample. Usually, OPF performance is analyzed based on measures computed from training and testing process, such as f-score and correct classification rate (accuracy). This paper proposes an approach based on visualization to support understanding of OPF training and testing processes. The visual approach uses multidimensional projection techniques to reduce the feature space dimensionality and to generate graphical representation from instances similarities. As a result, one can visualize, analyze and understand each step of OPF classifier: generation of the minimum-spanning tree, prototypes choosing, computation of optimum-path trees, and test samples classification. The experiments show that our approach is useful to understand how the prototypes are chosen, to identify what are the best prototypes, to visualize how the training dataset size influences the OPF performance, to analyze how a weak feature space can impact the OPF performance, and to identify some insights about OPF classifier as a whole.en
dc.description.affiliationSão Paulo State University (UNESP)
dc.description.affiliationUniversidade Do Oeste Paulista (UNOESTE)
dc.description.affiliationUnespSão Paulo State University (UNESP)
dc.format.extent777-782
dc.identifierhttp://dx.doi.org/10.1109/BRACIS.2019.00139
dc.identifier.citationProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019, p. 777-782.
dc.identifier.doi10.1109/BRACIS.2019.00139
dc.identifier.lattes8031012573259361
dc.identifier.orcid0000-0003-1248-528X
dc.identifier.scopus2-s2.0-85077040902
dc.identifier.urihttp://hdl.handle.net/11449/201429
dc.language.isoeng
dc.relation.ispartofProceedings - 2019 Brazilian Conference on Intelligent Systems, BRACIS 2019
dc.sourceScopus
dc.subjectExplainable artificial intelligence
dc.subjectMultidimensional projection
dc.subjectOptimum-path forest
dc.subjectVisualization assisted machine learning
dc.titleVisual approach to support analysis of optimum-path forest classifieren
dc.typeTrabalho apresentado em evento
unesp.author.lattes8031012573259361[3]
unesp.author.orcid0000-0003-1248-528X[3]
unesp.departmentMatemática e Computação - FCTpt

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