Simultaneous Feature and Model Selection for High-Dimensional Data

Author

Perolini, Alessandro ; Guérif, Sébastien

Author_Institution

Dipart. di Ing. Gestionale Piazza L. da Vinci, Politec. di Milano, Milan, Italy

fYear

2011

fDate

7-9 Nov. 2011

Firstpage

47

Lastpage

50

Abstract

The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters´ values which provide a low prediction error. Moreover, it does not require a pre-processing step to filter the features so it can be applied to a whole dataset.

Keywords

evolutionary computation; feature extraction; pattern classification; support vector machines; SVM parameters; artificial datasets; evolutionary-based method; high-dimensional data; prediction error; real-world datasets; simultaneous feature and model selection; support vector machine classifier prediction performance; Biological cells; Colon; Feature extraction; Genetic algorithms; Kernel; Support vector machines; Training; Feature selection; Support Vector Machines; classification performance; model selection;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on

Conference_Location

Boca Raton, FL

ISSN

1082-3409

Print_ISBN

978-1-4577-2068-0

Electronic_ISBN

1082-3409

Type

conf

DOI

10.1109/ICTAI.2011.16

Filename

6103305