%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.21.22.19
%2 sid.inpe.br/sibgrapi/2017/08.21.22.19.24
%@doi 10.1109/SIBGRAPI.2017.23
%T Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization
%D 2017
%A Rodrigues, Douglas,
%A Souza, André Nunes,
%A Papa, João Paulo,
%@affiliation Universidade Federal de São Carlos
%@affiliation Universidade Estadual de São Paulo
%@affiliation Universidade Estadual de São Paulo
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Optimum-Path Forest, Meta-heuristic Multi-objective Optimization, Prototype Selection.
%X 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 user-friendly.
%@language en
%3 paper.pdf