A Binary Water Wave Optimization Algorithm Applied to Feature Selection

  • Fagner Macêdo Instituto Federal do Ceará
  • Gabriel Barbosa Instituto Federal do Ceará
  • Ajalmar da Rocha Neto Instituto Federal do Ceará

Resumo


Neste trabalho, o problema de seleção de características é abordado através da introdução de uma nova versão binária para o algoritmo Water Wave Optimization (WWO), chamada Binary Water Wave Optimization (BWWO). O WWO, em sua versão original, é utilizado apenas para resolver problemas de otimização contínuos. O método aqui proposto combina as características de otimização presentes no WWO juntamente com a velocidade de treinamento do algoritmo Optimum-Path Forest (OPF) a fim de providenciar um framework capaz de resolver problemas de seleção de características, que são problemas discretos, de forma eficaz. Para avaliar o desempenho do BWWO, uma análise comparativa é feita com métodos clássicos de redução de dimensionalidade, mais especificamente com Principal Component Analysis (PCA) e Linear Discriminant Analysis (LDA). Com base nos experimentos, pode-se afirmar que BWWO é uma alternativa válida para problemas de seleção de características.

Palavras-chave: Water Wave Optimization, Seleção de Características, Redução de Dimensionalidade

Referências

Manickam, S., Balamurugan, R., and Lakshminarasimman, L. (2016). Water wave optimization algorithm for solving combined economic and emission dispatch problem. 12.

Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468), pages 41–48. Ieee.

Papa, J., Falcão, A., and Suzuki, C. (2009a). Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology, 19:120 – 131.

Papa, J. P., Falcao, A. X., and Suzuki, C. T. (2009b). Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology, 19(2):120–131.

Papa, J. P., Falcão, A. X., de Albuquerque, V. H. C., and Tavares, J. M. R. (2012). Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition, 45(1):512 – 520.

Prudhvi, R. B., Sai, K. V., Abhishek, Y., and Venkatanareshbabu, K. (2018). Feature selection using binary psogsa and radial basis network with a novel fitness function. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–6.

Raschka, S. (2015). Python Machine Learning. Packt Publishing.

Rodrigues, D., Pereira, L. A., Nakamura, R. Y., Costa, K. A., Yang, X.-S., Souza, A. N., and Papa, J. P. (2014). A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Systems with Applications, 41(5):2250 – 2258.

Rodrigues, D., Pereira, L. A. M., Almeida, T. N. S., Papa, J. P., Souza, A. N., Ramos, C. C. O., and Yang, X. (2013). Bcs: A binary cuckoo search algorithm for feature selection. In 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), pages 465–468.

Van Der Maaten, L., Postma, E., and Van den Herik, J. (2009). Dimensionality reduction: a comparative. J Mach Learn Res, 10(66-71):13.

Verónica, B.-C., Noelia, S.-M., and Amparo, A.-B. (2015). Feature Selection for HigDimensional Data.

Wu, X.-B., Liao, J., and Wang, Z.-C. (2015). Water wave optimization for the traveling salesman problem. 9225:137–146.

Zhao, F., Liu, H., Zhang, Y., Ma, W., and Zhang, C. (2018). A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Systems with Applications, 91:347 – 363.

Zheng, Y.-J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55:1 – 11.

NAcomo compará-lo com outras metaheurı́sticas utilizadas em problemas binários.

Manickam, S., Balamurugan, R., and Lakshminarasimman, L. (2016). Water wave optimization algorithm for solving combined economic and emission dispatch problem. 12.

Mika, S., Ratsch, G., Weston, J., Scholkopf, B., and Mullers, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468), pages 41–48. Ieee.

Papa, J., Falcão, A., and Suzuki, C. (2009a). Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology, 19:120 – 131.

Papa, J. P., Falcao, A. X., and Suzuki, C. T. (2009b). Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology, 19(2):120–131.

Papa, J. P., Falcão, A. X., de Albuquerque, V. H. C., and Tavares, J. M. R. (2012). Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition, 45(1):512 – 520.

Prudhvi, R. B., Sai, K. V., Abhishek, Y., and Venkatanareshbabu, K. (2018). Feature selection using binary psogsa and radial basis network with a novel fitness function. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pages 1–6.

Raschka, S. (2015). Python Machine Learning. Packt Publishing.

Rodrigues, D., Pereira, L. A., Nakamura, R. Y., Costa, K. A., Yang, X.-S., Souza, A. N., and Papa, J. P. (2014). A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Systems with Applications, 41(5):2250 – 2258.

Rodrigues, D., Pereira, L. A. M., Almeida, T. N. S., Papa, J. P., Souza, A. N., Ramos, C. C. O., and Yang, X. (2013). Bcs: A binary cuckoo search algorithm for feature selection. In 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013), pages 465–468.

Van Der Maaten, L., Postma, E., and Van den Herik, J. (2009). Dimensionality reduction: a comparative. J Mach Learn Res, 10(66-71):13.

Verónica, B.-C., Noelia, S.-M., and Amparo, A.-B. (2015). Feature Selection for HigDimensional Data.

Wu, X.-B., Liao, J., and Wang, Z.-C. (2015). Water wave optimization for the traveling salesman problem. 9225:137–146.

Zhao, F., Liu, H., Zhang, Y., Ma, W., and Zhang, C. (2018). A discrete water wave optimization algorithm for no-wait flow shop scheduling problem. Expert Systems with Applications, 91:347 – 363.

Zheng, Y.-J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55:1 – 11.
Publicado
15/10/2019
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MACÊDO, Fagner; BARBOSA, Gabriel; ROCHA NETO, Ajalmar da. A Binary Water Wave Optimization Algorithm Applied to Feature Selection. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 448-459. DOI: https://doi.org/10.5753/eniac.2019.9305.