Autor(es):
Eler, Danilo Medeiros [UNESP] ; Batista, Matheus Prachedes [UNESP] ; Garcia, Rogério Eduardo [UNESP] ; Pereira, Danillo Roberto ; Marcilio, Wilson Estecio [UNESP]
Data: 2020
Identificador Persistente: http://hdl.handle.net/11449/201429
Origem: Oasisbr
Assunto(s): Explainable artificial intelligence; Multidimensional projection; Optimum-path forest; Visualization assisted machine learning; Explainable artificial intelligence; Explainable artificial intelligence; Multidimensional projection; Multidimensional projection; Optimum-path forest; Optimum-path forest; Visualization assisted machine learning; Visualization assisted machine learning
Descrição
Made available in DSpace on 2020-12-12T02:32:20Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-10-01
Optimum-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.
São Paulo State University (UNESP)
Universidade Do Oeste Paulista (UNOESTE)
São Paulo State University (UNESP)
