A Comparative Study on Morphological Neural Networks for Binary Classification
Resumo
Redes neurais morfológicas representam uma classe de redes neurais artificiais cujos neurônios efetuam uma operação da morfologia matemática seguida da aplicação de uma função de ativação. Este artigo apresenta um estudo comparativo de diferentes abordagens que utilizam redes neurais morfológicas. Especificamente, de acordo com a regra de treinamento, revisamos abordagens incrementais, baseadas no método de máxima descida, máquinas de aprendizado extremo e procedimento de otimização convexa-côncava. Experimentos computacionais mostraram que, em média, o perceptron erosão-dilatação reduzido com as estratégias bagging e ensemble obteve melhores resultados em diversos problemas de classificação binária.
Referências
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Charisopoulos, V. and Maragos, P. (2017). Morphological perceptrons: Geometry and training algorithms. In Angulo, J., Velasco-Forero, S., and Meyer, F., editors, Mathematical Morphology and Its Applications to Signal and Image Processing, pages 3–15, Cham. Springer International Publishing.
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Ritter, G. X. and Sussner, P. (1996). An Introduction to MorphoIn Proceedings of the 13th International Conference on logical Neural Networks. Pattern Recognition, pages 709–717, Vienna, Austria.
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Sussner, P. (1998). Morphological perceptron learning. In IEEE International Symposium on Intelligent Control Proceedings, pages 477–482. IEEE.
Sussner, P. and Campiotti, I. (2020). Extreme learning machine for a new hybrid morphological/linear perceptron. Neural Networks, 123:288 – 298.
Sussner, P. and Esmi, E. L. (2011). Morphological Perceptrons with Competitive Learning: Lattice-Theoretical Framework and Constructive Learning Algorithm. Information Sciences, 181(10):1929–1950.
Valle, M. E. (2020). Reduced dilation-erosion perceptron for binary classification. Mathematics, 8(4):512.
Charisopoulos, V. and Maragos, P. (2017). Morphological perceptrons: Geometry and training algorithms. In Angulo, J., Velasco-Forero, S., and Meyer, F., editors, Mathematical Morphology and Its Applications to Signal and Image Processing, pages 3–15, Cham. Springer International Publishing.
de A. Araújo, R. (2011). A class of hybrid morphological perceptrons with application in time series forecasting. Knowledge-Based Systems, 24(4):513 – 529.
Franchi, G., Fehri, A., and Yao, A. (2020). Deep morphological networks. Pattern Recognition, 102:107246.
Haykin, S. (2009). Neural Networks and Learning Machines. Prentice-Hall, Upper Saddle River, NJ, 3rd edition edition.
Hernández, G., Zamora, E., Sossa, H., Téllez, G., and Furlán, F. (2019). Hybrid neural networks for big data classification. Neurocomputing, page S0925231219314560.
Huang, G., Huang, G. B., Song, S., and You, K. (2015). Trends in extreme learning machines: A review.
Mondal, R., Santra, S., and Chanda, B. (2019). Dense morphological network: An universal function approximator.
Pessoa, L. and Maragos, P. (2000). Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition. Pattern Recognition, 33:945–960.
Ritter, G. X. and Sussner, P. (1996). An Introduction to MorphoIn Proceedings of the 13th International Conference on logical Neural Networks. Pattern Recognition, pages 709–717, Vienna, Austria.
Ritter, G. X. and Urcid, G. (2003). Lattice Algebra Approach to Single-Neuron Computation. IEEE Transactions on Neural Networks, 14(2):282–295.
Sussner, P. (1998). Morphological perceptron learning. In IEEE International Symposium on Intelligent Control Proceedings, pages 477–482. IEEE.
Sussner, P. and Campiotti, I. (2020). Extreme learning machine for a new hybrid morphological/linear perceptron. Neural Networks, 123:288 – 298.
Sussner, P. and Esmi, E. L. (2011). Morphological Perceptrons with Competitive Learning: Lattice-Theoretical Framework and Constructive Learning Algorithm. Information Sciences, 181(10):1929–1950.
Valle, M. E. (2020). Reduced dilation-erosion perceptron for binary classification. Mathematics, 8(4):512.
Publicado
29/11/2021
Como Citar
BARROS, Luana Felipe de; VALLE, Marcos Eduardo.
A Comparative Study on Morphological Neural Networks for Binary Classification. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 655-666.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18292.
