Título

Fisher’s decision tree

Autor

ASDRUBAL LOPEZ CHAU

Jair Cervantes Canales

LOURDES LOPEZ GARCIA

Farid García Lamont

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Oblique decision tree - ([0957-4174]) Fisher's linear discriminant - ([0957-4174]) C4.5 - ([0957-4174]) INGENIERÍA Y TECNOLOGÍA - (CTI)

Resumen o descripción

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way. The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.

Editor

Expert Systems with Applications

Fecha de publicación

15 de noviembre de 2013

Tipo de publicación

Artículo

Recurso de información

http://hdl.handle.net/20.500.11799/41191

Fuente

0957-4174

Idioma

Inglés

Relación

10.1016/j.eswa.2013.05.044;

Audiencia

Estudiantes

Investigadores

Repositorio Orígen

REPOSITORIO INSTITUCIONAL DE LA UAEM

Descargas

974