Resumo

GALINDO, Eiber A.; PERDOMO, Jairo A.  e  FIGUEROA-GARCIA, Juan C.. Comparative study among multiclass support vector machines, artificial neural networks and self-organized neuro-fuzzy inference system for classification problems. Inf. tecnol. [online]. 2020, vol.31, n.1, pp.273-286. ISSN 0718-0764.  http://dx.doi.org/10.4067/S0718-07642020000100273.

In this paper an explanation of the structure and how a self-organized neuro-fuzzy inference system (SONFIS) works, is given with detail. The study uses three classification problems (Fisher iris, Breast Cancer and Human Activities) to then compare the results with well-known universal classifiers such as artificial neural networks (ANN) and multiclass support vector machines (SVM). A brief description of each of these methods is presented. The results show that SONFIS has a similar, and sometimes better, performance than ANN and SVM with the advantage of generating a rule basis that helps understanding the inner structure of the problem.

Palavras-chave : fuzzy logic; intelligent algorithms; support vector machines; neural networks..

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