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

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

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

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