Permanent Disability Classification by Combining Evolutionary Generalized Radial Basis Function and Logistic Regression Methods

Research areas:

• Methodology - Machine learning

Year:

2012

Type of Publication:

Article

Keywords:

permanent disability, logistic regression

Authors:

• Castaño-Méndez, Adiel
• Fernandez-Navarro, Francisco
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Expert Systems with Applications

Volume:

39

Number:

9

Pages:

8350-8355

ISSN:

0957-4174

BibTex:

@article{ESWA2012, author = "Adiel Casta{\~n}o-M{\'e}ndez and Francisco Fernandez-Navarro and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Recently, a novelty multinomial logistic regression method where the initial covariate space is increased by adding the nonlinear transformations of the input variables given by Gaussian Radial Basis Functions (RBFs) obtained by an evolutionary algorithm was proposed. However, there still exist some problems with the standard Gaussian RBF, for example, the approximation of constant valued functions or the approximation of high dimensionality associated to some real problems. In order to face these problems, we propose the use of the generalized Gaussian RBF (GRBF) instead of the standard Gaussian RBF. Our approach has been validated with a real problem of disability classification, to evaluate its effectiveness. Experimental results show that this approach is able to achieve good generalization performance.", awards = "JCR (2012): 1.854 (category OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE, position 13/79 Q1)", comments = "JCR (2012): 1.854 (category OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE, position 13/79 Q1)", doi = "10.1016/j.eswa.2012.01.186", issn = "0957-4174", journal = "Expert Systems with Applications", keywords = "permanent disability, logistic regression", note = "JCR (2012): 1.854 (category OPERATIONS RESEARCH {\&}amp; MANAGEMENT SCIENCE, position 13/79 Q1)", number = "9", pages = " 8350--8355", title = "{P}ermanent {D}isability {C}lassification by {C}ombining {E}volutionary {G}eneralized {R}adial {B}asis {F}unction and {L}ogistic {R}egression {M}ethods", url = "http://dx.doi.org/10.1016/j.eswa.2012.01.186", volume = "39", year = "2012", }

Note:

JCR (2012): 1.854 (category OPERATIONS RESEARCH & MANAGEMENT SCIENCE, position 13/79 Q1)

Abstract:

Recently, a novelty multinomial logistic regression method where the initial covariate space is increased by adding the nonlinear transformations of the input variables given by Gaussian Radial Basis Functions (RBFs) obtained by an evolutionary algorithm was proposed. However, there still exist some problems with the standard Gaussian RBF, for example, the approximation of constant valued functions or the approximation of high dimensionality associated to some real problems. In order to face these problems, we propose the use of the generalized Gaussian RBF (GRBF) instead of the standard Gaussian RBF. Our approach has been validated with a real problem of disability classification, to evaluate its effectiveness. Experimental results show that this approach is able to achieve good generalization performance.

Comments:

JCR (2012): 1.854 (category OPERATIONS RESEARCH & MANAGEMENT SCIENCE, position 13/79 Q1)