A Binary Water Wave Optimization Algorithm Applied to Feature Selection
Abstract
In this work, the problem of feature selection is assessed by introducting a new binary version for the Water Wave Optimization (WWO) algorithm, called Binary Water Wave Optimization (BWWO). The WWO algorithm, in its original version, is used to solve only continuos optimization problems. The method proposed here combines the optimization characteristics of WWO with the speed of the optimum-path forest (OPF) classifier in order to provide a framework able to solve the feature selection task, which is a discrete problem, in an efficient way. In order to evaluate the performance obtained by BWWO, a comparative analysis against traditional methods of dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is made. On the basis of our experiments, we can say that BWWO is a valid alternative for feature selection tasks.
Keywords:
Water Wave Optimization, Feature Selection, Dimensionality Reduction
References
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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.
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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.
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.
Published
2019-10-15
How to Cite
MACÊDO, Fagner; BARBOSA, Gabriel; ROCHA NETO, Ajalmar da.
A Binary Water Wave Optimization Algorithm Applied to Feature Selection. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 448-459.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9305.
