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		<identifier>8JMKD3MGPAW/3PFRFCH</identifier>
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		<doi>10.1109/SIBGRAPI.2017.23</doi>
		<citationkey>RodriguesSouzPapa:2017:PrOpFo</citationkey>
		<title>Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
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		<author>Rodrigues, Douglas,</author>
		<author>Souza, André Nunes,</author>
		<author>Papa, João Paulo,</author>
		<affiliation>Universidade Federal de São Carlos</affiliation>
		<affiliation>Universidade Estadual de São Paulo</affiliation>
		<affiliation>Universidade Estadual de São Paulo</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>douglasrodrigues.dr@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ, Brazil</conferencelocation>
		<date>17-20 Oct. 2017</date>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<booktitle>Proceedings</booktitle>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<versiontype>finaldraft</versiontype>
		<keywords>Optimum-Path Forest, Meta-heuristic Multi-objective Optimization, Prototype Selection.</keywords>
		<abstract>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.</abstract>
		<language>en</language>
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		<usergroup>douglasrodrigues.dr@gmail.com</usergroup>
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