Título
A hybrid algorithm to improve the accuracy of support vector machines on skewed data-sets
Autor
Jair Cervantes Canales
Farid García Lamont
ASDRUBAL LOPEZ CHAU
De-Shuang Huang
Nivel de Acceso
Acceso Abierto
Materias
Resumen o descripción
Over the past few years, has been shown that generalization power of Support Vector Machines (SVM) falls dramatically on imbalanced data-sets. In this paper, we propose a new method to improve accuracy of SVM on imbalanced data-sets. To get this outcome, firstly, we used undersampling and SVM to obtain the initial SVs and a sketch of the hyperplane. These support vectors help to generate new artificial instances, which will take part as the initial population of a genetic algorithm. The genetic algorithm improves the population in artificial instances from one generation to another and eliminates instances that produce noise in the hyperplane. Finally, the generated and evolved data were included in the original data-set for minimizing the imbalance and improving the generalization ability of the SVM on skewed data-sets.
Editor
Springer
Fecha de publicación
2014
Tipo de publicación
Capítulo de libro
Recurso de información
Fuente
0302-9743
978-3-319-09332-1
Idioma
Inglés
Relación
10.1007/978-3-319-09333-8_85;
Audiencia
Estudiantes
Investigadores
Repositorio Orígen
REPOSITORIO INSTITUCIONAL DE LA UAEM
Descargas
541