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• W2767753278 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." @default.
• W2767753278 created "2017-11-17" @default.
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• W2767753278 date "2017-10-01" @default.
• W2767753278 modified "2023-10-02" @default.
• W2767753278 title "Pruning Optimum-Path Forest Classifiers Using Multi-Objective Optimization" @default.
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• W2767753278 doi "https://doi.org/10.1109/sibgrapi.2017.23" @default.
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