ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Mario Antonio Ruz Canul
Universidad de Guadalajara
0000-0003-0872-062X
José A. Ruz Hernandez
Universidad Autónoma del Carmen
0000-0001-8332-4980
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Ramón García Hernández
Instituto Tecnólogico de La Laguna
0000-0003-0602-8795
José Luis Rullan Lara
Universidad Autónoma del Carmen
0000-0002-6007-1025
Acerca de
This paper presents a modification to the Optimal Hyper- plane synthesis to improve the linear separating properties of Support Vector Machines. The proposed modification is based on a modified op- timization criterion, which is extended to non-linearly separable train- ing data via the Soft Margin Optimal Hyperplane. Performance metrics demonstrate that the proposed modification maximizes the margin dur- ing hyperplane construction by increasing the average distance of both training patterns and support vectors, thereby improving classification accuracy.
Referencias
Abe, S., Support Vector Machines for Pattern Classification, Advances in Pattern Recognition, (2005) doi:10.1007/978-1-84996-098-4 1
Chandra, M.A., Bedi, S.S. Survey on SVM and their application in image classification. Int. j. inf. tecnol. 13, 1–11 (2021). doi:10.1007/s41870-017-0080-1
Cortes, C., Vapnik V. N., Support vector networks. Machine Learning. 20, (1995) 273-297. doi:10.1007/BF00994018
Lo C-S., Wang C-M. Support vector machine for breast MR image classification. Comput Math Appl 64:1153–1162 (2012) doi:10.1016/j.camwa.2012.03.033
Homaeinezhad, M. R., Tavakkoli, E., Atyabi, S. A., Ghaffari, A., and Ebrahimpour,
R. . Synthesis of multiple-type classification algorithms for robust heart rhythm type recognition: Neuro-svm-pnn learning machine with virtual QRS image-based geometrical features. Scientia Iranica, 18(3), 423-431. (2011). doi:10.1016/j.scient.
2011.05.033.
Bansal, M., Goyal, A., and Choudhary, A. A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term mem- ory algorithms in machine learning. Decision Analytics Journal, 3, 100071. (2022). doi:10.1016/j.dajour.2022.100071
Zhang, H., Shi, Y., Yang, X., and Zhou, R. A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Research in International Business and Finance, 58, 101482.(2021). doi:10.1016/j.ribaf.2021. 101482
Meng, E., Huang, S., Huang, Q., Fang, W., Wang, H., Leng, G., and Liang, H. A hybrid VMD-SVM model for practical streamflow prediction using an innovative input selection framework. Water Resources Management, 35, 1321-1337. (2021). doi:10.1007/s11269-021-02786-7
Divya, P., and Devi, B. A. Hybrid metaheuristic algorithm enhanced support vector machine for epileptic seizure detection. Biomedical Signal Processing and Control, 78, 103841. (2022). doi:10.1016/j.bspc.2022.103841
Laxmi, S., Gupta, S. K., and Kumar, S. Intuitionistic fuzzy least square twin support vector machines for pattern classification. Annals of Operations Research, 339(3), 1329-1378. (2024). doi:10.1007/s10479-022-04626-2
Wang, H. A novel feature selection method based on quantum support vector machine. Physica Scripta, 99(5), 056006.(2024) doi:10.1088/1402-4896/ad36ef
Vapnik V. N., Statistical Learning Theory. (John Wiley and Sons, New York, 1998).
Smith, J.W., Everhart, J.E., Dickson, W.C., Knowler, W.C., and Johannes, R.S. Using the ADAP learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Symposium on Computer Applications and Medical Care (pp. 261–265). IEEE Computer Society Press. (1988)
Aoki M. Introduction to Optimization Techniques. Fundamentals and Applications of Nonlinear Programming- (McMillan, USA, 1971).
Haykin S., Neural Networks: A comprehensive foundation, 2nd ed. (Prentice Hall, Upper-Saddle River, NJ, 1999).
Vapnik V. N., The Nature of Statistical Learning Theory, 2nd ed., (Springer-Verlag, USA, 1999)