Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization

Autor(es)

Rodrigues, Douglas ; Souza, Andre Nunes ; Papa, Joao Paulo ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2017-01-01.

Resumo

Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although OPF pruning can provide such a nice representation, it comes with the price of being parameter-dependent. The proposed approach cope with that problem by avoiding the classifier to be hand-tuned by modeling the task of parameter learning as a multi-objective-oriented optimization problem, which can be less prone to errors. Experiments on public datasets show the robustness of the proposed approach, which is now parameterless and userfriendly. (AU)

• 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