Improving optimum-path forest learning using bag-of-classifiers and confidence measures

Autor(es)

Nachif Fernandes, Silas Evandro ^[1] ; Papa, Joao Paulo ^[2]

Afiliação do(s) autor(es)

^[1] Fed Univ Sao Carlos UFSCar, Dept Comp, Rodovia Washington Luis, Km 235 SP 310, BR-13565905 Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Dept Comp, Av Engn Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PATTERN ANALYSIS AND APPLICATIONS; v. 22, n. 2, p. 703-716, MAY 2019.
• Citações Web of Science: 1

Resumo

Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this work, we presented an ensemble of optimum-path forest (OPF) classifiers, which consists into combining different instances that compute a score-based confidence level for each training sample in order to turn the classification process smarter, i.e., more reliable. Such confidence level encodes the level of effectiveness of each training sample, and it can be used to avoid ties during the OPF competition process. Experimental results over fifteen benchmarking datasets have shown the effectiveness and efficiency of the proposed approach for classification problems, with more accurate results in more than 67% of the datasets considered in this work. Additionally, we also considered a bagging strategy for comparison purposes, and we showed the proposed approach can lead to considerably better results. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Modalidade de apoio: Auxílio à Pesquisa - Regular