Affiliations: Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Belo Horizonte, MG, Brazil
Correspondence:
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Corresponding author. Alisson Marques Silva, Graduate Program in Mathematical and Computational Modeling, Federal Center of Technological Education of Minas Gerais, Av. Amazonas, 7675 - Nova Gameleira, Belo Horizonte, MG, Brazil 30510-000. E-mail: [email protected].
Abstract: Evolving models have shown great success in processing non-stationary data that change their characteristics over time. Motivated by elaborating a high-performance model for data classification, the present work proposes a new evolving fuzzy classifier. The proposed model, named evolving Fuzzy Mean Classifier (eFMC), has a low computational cost and is autonomous, i.e., no has user-defined parameters. The eFMC is based on fuzzy clustering structures, where the membership degree between the samples and the clusters is used to obtain the output. In the proposed approach, each class is represented by a cluster, and new clusters are created whenever a new class is discovered. The centers of the clusters are updated through the sample’s means calculated incrementally. Computational experiments were carried out to evaluate and compare the performance of the eFMC in terms of accuracy and processing time. Experimental results and comparisons against alternative state-of-the-art evolving classifiers show that the eFMC is accurate and fast, characteristics essential for adaptive classifiers, especially in online and real-time environments.
