Implementation of Genetic Programming in Binary Classification problems
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Algoritmos evolutivos, aprendizaje de máquina, clasificación binaria, programación genética
This work shows the implementation of genetic programming to solve binary classification problems. One of the objectives of this work is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data (supervised learning). Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.
Keywords: Binary classification, evolutionary algorithms, genetic programming, machine learning
J. Pabón-Gómez, “Las TICs y la lúdica como herramientas facilitadoras en el aprendizaje de la matemática”, Eco Matemático, vol. 5, n.º 1, pp. 37-48, ene. 2014.
https://doi.org/10.22463/17948231.62 DOI: https://doi.org/10.22463/17948231.62
N. Hernández y A. Flórez-Fuentes, "Computación en la Nube", Mundo FESC, vol. 4, n.º 8, pp. 46-51, dic. 2014.
A. A. Holts-Corey y C.L. Riquelme-Jerez. “Desarrollo de un sistema de clasificación binaria automática de noticias con máquinas de aprendizaje.” Tesis de maestria, Pontificia Universidad Católica de Valparaíso, Chile, 2010.
B. Mohamed, A. Issam, A. Mohamed y B. Abdellatif, “ECG image classification in real time based on the Haar-like features and artificial neural networks.” Procedia Computer Science, vol. 73, pp. 32-39, 2015. DOI: https://doi.org/10.1016/j.procs.2015.12.045
T.V.N. Nidhin-Prabhakar, G. Xavier, P. Geetha and K.P. Soman, “Spatial preprocessing based multiomial logistic regression for hyperspectral image classification.” Procedia Computer Science, vol. 46, pp. 1817-1826, 2015. DOI: https://doi.org/10.1016/j.procs.2015.02.140
K. Parikh and T.P. Shah, “Support vector machine - A large margin classifier to diagnose skin illness.” Procedia Technology, vol. 23, pp. 369-375, 2016. DOI: https://doi.org/10.1016/j.protcy.2016.03.039
J.P Patel, and S.H Upadhyay, “Comparison between artificial neural network and support vector method for a fault diagnosis in rolling element bearings”. Procedia Engineering, 144, 390-397, 2016. DOI: https://doi.org/10.1016/j.proeng.2016.05.148
I.N. Yulita, M.I. Fanany and A.M. Arymuthy, “Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification.” Procedia Computer Science, vol. 116, pp. 530-538, 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.042
J. Rojas Gómez, "El pensamiento Abstracto a partir de la interdisciplinariedad de las Matemáticas", Eco Matemático, vol. 8, pp. 51-53, jun. 2018.
https://doi.org/10.22463/17948231.1382 DOI: https://doi.org/10.22463/17948231.1382
M. Largo-Leal, P. Jaimes-Espinoza, y Y. Largo-Leal, "Abordando el aprendizaje de las matemáticas", Eco Matemático, vol. 5, n.º 1, pp. 60-65, ene. 2014. https://doi.org/10.22463/17948231.53 DOI: https://doi.org/10.22463/17948231.53
JC. Hernández-Suarez, L. Jaimes-Contreras, y R. Chaves-Escobar, "Modelos de aplicación de ecuaciones diferenciales de primer orden con geogebra: actividades para resolver problemas de mezclas", Mundo Fesc, vol. 6, n.º 11, pp. 7-15, sep. 2016.
J.F. Diaz-Cordova, E. Coba-Molina and P. Navarrete-López, “Fuzzy logic and financial risk. A propossed classification of financila risk to the cooperative sector.” Contaduría y Administración, vol. 62, pp. 33-34, 2017. DOI: https://doi.org/10.1016/j.cya.2017.10.001
S. Murmu and S. Biswas. “Application of fuzzy logic and neural network in crop classification.” Aquatic Procedia, vol. 4, pp. 1203-1210, 2015. DOI: https://doi.org/10.1016/j.aqpro.2015.02.153
Y. Medina Vargas y H. Miranda Mnedez, "Comparación de algoritmos basados en la criptografía simétrica DES, AES y 3DES", Mundo Fesc, vol. 5, n.º 9, pp. 14-21, dic. 2015.
P.G. Espejo, S. Ventura and F. Herrera, “A Survey on the Application of Genetic Programming to Classification.” IEEE Transactions of Systems, Man, and Cybernetics, vol. 40, pp. 121-144, 2009. DOI: https://doi.org/10.1109/TSMCC.2009.2033566
J. Koza. Genetic programming: On the programming of computers by means of natural evolution. Cambridge: MIT Press, 1992.
R. Poli, W.B. Langdon and N.F. McPhee, A field guide to genetic programming. San Francisco: Lulu Enterprises, 2008.
E.Z. Flores, L. Trujillo, O. Schütze and P. Legrand, “Evaluating the effects of local search in genetic programming.” En EVOLVE-A bridge between probability, set oriented numerics, and evolutionay computation V, 2014, pp. 213-228. DOI: https://doi.org/10.1007/978-3-319-07494-8_15
D. Liu, T. Li and D. Liang,“Incorporating logistic regression to decision-theoretic rough sets for classifi cations.” International Journal of Approximate Reasoning, vol. 55, pp. 197-210, 2014. DOI: https://doi.org/10.1016/j.ijar.2013.02.013
E.Z. Flores, L. Trujillo, O. Schutze and P. Legrand. “A local search approach to genetic programming for binary classification.” En Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1151-1158, 2015. DOI: https://doi.org/10.1145/2739480.2754797
J. Eggermont, A.E. Eiben and J.I. Hemert, “Adapting the fitness function in GP for data mining.” En Proceedings of the Second European Workshop on Genetic Programming, pp. 193-202, 1999. DOI: https://doi.org/10.1007/3-540-48885-5_16
S.M. Winkler, M. Affenzeller and S. Wagner. “Advanced Genetic Programming Based Machine Learning.” Journal of Mathematical Modelling and Algorithms, vol. 6, pp. 455-480, 2007. DOI: https://doi.org/10.1007/s10852-007-9065-6
D. Dua and E. Taniskidou, “UCI Machine Learning Repository.” Internet: http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science, 2017.
GPLAB A Genetic Programming Toolbox for MATLAB, 2017. [En línea]. Disponible en http://gplab.sourceforge.net/.
Y. M. Moreno-Sánchez, R. M. García-Manrique, G. R. Robles-Gil y J. K. Porras-Lara,“Valoración del riesgo biopsicosocial en gestantes de Cúcuta”, Aibi revista de investigación, administración e ingeniería, vol. 7, nº 1, pp. 19-22, 2019. DOI: https://doi.org/10.15649/2346030X.508
B. N. Arias, “El consumo responsable: educar para la sostenibilidad ambiental”, Revista AiBi, vol. 4, nº 1, pp. 32-37, 2016 DOI: https://doi.org/10.15649/2346030X.385
This work shows the implementation of genetic programming to solve binary classification problems. One of the objectives of this work is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data (supervised learning). Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.
Keywords: Binary classification, evolutionary algorithms, genetic programming, machine learning
J. Pabón-Gómez, “Las TICs y la lúdica como herramientas facilitadoras en el aprendizaje de la matemática”, Eco Matemático, vol. 5, n.º 1, pp. 37-48, ene. 2014.
https://doi.org/10.22463/17948231.62 DOI: https://doi.org/10.22463/17948231.62
N. Hernández y A. Flórez-Fuentes, "Computación en la Nube", Mundo FESC, vol. 4, n.º 8, pp. 46-51, dic. 2014.
A. A. Holts-Corey y C.L. Riquelme-Jerez. “Desarrollo de un sistema de clasificación binaria automática de noticias con máquinas de aprendizaje.” Tesis de maestria, Pontificia Universidad Católica de Valparaíso, Chile, 2010.
B. Mohamed, A. Issam, A. Mohamed y B. Abdellatif, “ECG image classification in real time based on the Haar-like features and artificial neural networks.” Procedia Computer Science, vol. 73, pp. 32-39, 2015. DOI: https://doi.org/10.1016/j.procs.2015.12.045
T.V.N. Nidhin-Prabhakar, G. Xavier, P. Geetha and K.P. Soman, “Spatial preprocessing based multiomial logistic regression for hyperspectral image classification.” Procedia Computer Science, vol. 46, pp. 1817-1826, 2015. DOI: https://doi.org/10.1016/j.procs.2015.02.140
K. Parikh and T.P. Shah, “Support vector machine - A large margin classifier to diagnose skin illness.” Procedia Technology, vol. 23, pp. 369-375, 2016. DOI: https://doi.org/10.1016/j.protcy.2016.03.039
J.P Patel, and S.H Upadhyay, “Comparison between artificial neural network and support vector method for a fault diagnosis in rolling element bearings”. Procedia Engineering, 144, 390-397, 2016. DOI: https://doi.org/10.1016/j.proeng.2016.05.148
I.N. Yulita, M.I. Fanany and A.M. Arymuthy, “Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification.” Procedia Computer Science, vol. 116, pp. 530-538, 2017. DOI: https://doi.org/10.1016/j.procs.2017.10.042
J. Rojas Gómez, "El pensamiento Abstracto a partir de la interdisciplinariedad de las Matemáticas", Eco Matemático, vol. 8, pp. 51-53, jun. 2018.
https://doi.org/10.22463/17948231.1382 DOI: https://doi.org/10.22463/17948231.1382
M. Largo-Leal, P. Jaimes-Espinoza, y Y. Largo-Leal, "Abordando el aprendizaje de las matemáticas", Eco Matemático, vol. 5, n.º 1, pp. 60-65, ene. 2014. https://doi.org/10.22463/17948231.53 DOI: https://doi.org/10.22463/17948231.53
JC. Hernández-Suarez, L. Jaimes-Contreras, y R. Chaves-Escobar, "Modelos de aplicación de ecuaciones diferenciales de primer orden con geogebra: actividades para resolver problemas de mezclas", Mundo Fesc, vol. 6, n.º 11, pp. 7-15, sep. 2016.
J.F. Diaz-Cordova, E. Coba-Molina and P. Navarrete-López, “Fuzzy logic and financial risk. A propossed classification of financila risk to the cooperative sector.” Contaduría y Administración, vol. 62, pp. 33-34, 2017. DOI: https://doi.org/10.1016/j.cya.2017.10.001
S. Murmu and S. Biswas. “Application of fuzzy logic and neural network in crop classification.” Aquatic Procedia, vol. 4, pp. 1203-1210, 2015. DOI: https://doi.org/10.1016/j.aqpro.2015.02.153
Y. Medina Vargas y H. Miranda Mnedez, "Comparación de algoritmos basados en la criptografía simétrica DES, AES y 3DES", Mundo Fesc, vol. 5, n.º 9, pp. 14-21, dic. 2015.
P.G. Espejo, S. Ventura and F. Herrera, “A Survey on the Application of Genetic Programming to Classification.” IEEE Transactions of Systems, Man, and Cybernetics, vol. 40, pp. 121-144, 2009. DOI: https://doi.org/10.1109/TSMCC.2009.2033566
J. Koza. Genetic programming: On the programming of computers by means of natural evolution. Cambridge: MIT Press, 1992.
R. Poli, W.B. Langdon and N.F. McPhee, A field guide to genetic programming. San Francisco: Lulu Enterprises, 2008.
E.Z. Flores, L. Trujillo, O. Schütze and P. Legrand, “Evaluating the effects of local search in genetic programming.” En EVOLVE-A bridge between probability, set oriented numerics, and evolutionay computation V, 2014, pp. 213-228. DOI: https://doi.org/10.1007/978-3-319-07494-8_15
D. Liu, T. Li and D. Liang,“Incorporating logistic regression to decision-theoretic rough sets for classifi cations.” International Journal of Approximate Reasoning, vol. 55, pp. 197-210, 2014. DOI: https://doi.org/10.1016/j.ijar.2013.02.013
E.Z. Flores, L. Trujillo, O. Schutze and P. Legrand. “A local search approach to genetic programming for binary classification.” En Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1151-1158, 2015. DOI: https://doi.org/10.1145/2739480.2754797
J. Eggermont, A.E. Eiben and J.I. Hemert, “Adapting the fitness function in GP for data mining.” En Proceedings of the Second European Workshop on Genetic Programming, pp. 193-202, 1999. DOI: https://doi.org/10.1007/3-540-48885-5_16
S.M. Winkler, M. Affenzeller and S. Wagner. “Advanced Genetic Programming Based Machine Learning.” Journal of Mathematical Modelling and Algorithms, vol. 6, pp. 455-480, 2007. DOI: https://doi.org/10.1007/s10852-007-9065-6
D. Dua and E. Taniskidou, “UCI Machine Learning Repository.” Internet: http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science, 2017.
GPLAB A Genetic Programming Toolbox for MATLAB, 2017. [En línea]. Disponible en http://gplab.sourceforge.net/.
Y. M. Moreno-Sánchez, R. M. García-Manrique, G. R. Robles-Gil y J. K. Porras-Lara,“Valoración del riesgo biopsicosocial en gestantes de Cúcuta”, Aibi revista de investigación, administración e ingeniería, vol. 7, nº 1, pp. 19-22, 2019. DOI: https://doi.org/10.15649/2346030X.508
B. N. Arias, “El consumo responsable: educar para la sostenibilidad ambiental”, Revista AiBi, vol. 4, nº 1, pp. 32-37, 2016 DOI: https://doi.org/10.15649/2346030X.385
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