Souza, Renato William R. deOliveira, Joao Vitor Chaves dePassos, Leandro A. [UNESP]Ding, WeipingPapa, Joao P. [UNESP]Albuquerque, Victor Hugo C. de2021-06-252021-06-252020-12-01Ieee Transactions On Fuzzy Systems. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 28, n. 12, p. 3076-3086, 2020.1063-6706http://hdl.handle.net/11449/210572In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for supervised, semisupervised, and unsupervised learning, named optimum-path forest (OPF), was proposed with competitive results in several applications, besides comprising a low computational burden. In this article, we propose the fuzzy OPF, an improved version of the standard OPF classifier, that learns the samples' membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over 12 public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst case scenarios.3076-3086engTrainingPrototypesForestryStandardsSupport vector machinesFuzzy logicClustering algorithmsClassifiersfuzzyoptimum-path forest (OPF)pattern recognitionArtigo10.1109/TFUZZ.2019.2949771WOS:000595527100003