Result: Multi-objective parameters selection for SVM classification using NSGA-II

Title:
Multi-objective parameters selection for SVM classification using NSGA-II
Authors:
LI XU, CHUNPING LI
Source:
Advances in data mining (applications in medicine, web mining, marketing, image and signal mining)0ICDM 2006. :365-376
Publisher Information:
Berlin; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 21 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Systèmes d'information. Bases de données, Information systems. Data bases, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse statistique, Statistical analysis, Análisis estadístico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Classification, Clasificación, Fonction erreur, Error function, Función error, Fouille donnée, Data mining, Busca dato, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Méthode adaptative, Adaptive method, Método adaptativo, Problème sélection, Selection problem, Problema selección, Programmation mathématique, Mathematical programming, Programación matemática, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Borne erreur, Error bound, Limite error
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Software, Tsinghua University, China
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19131457
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19131457
Database:
PASCAL Archive

Further Information

Selecting proper parameters is an important issue to extend the classification ability of Support Vector Machine (SVM), which makes SVM practically useful. Genetic Algorithm (GA) has been widely applied to solve the problem of parameters selection for SVM classification due to its ability to discover good solutions quickly for complex searching and optimization problems. However, traditional GA in this field relys on single generalization error bound as fitness function to select parameters. Since there have several generalization error bounds been developed, picking and using single criterion as fitness function seems intractable and insufficient. Motivated by the multi-objective optimization problems, this paper introduces an efficient method of parameters selection for SVM classification based on multi-objective evolutionary algorithm NSGA-II. We also introduce an adaptive mutation rate for NSGA-II. Experiment results show that our method is better than single-objective approaches, especially in the case of tiny training sets with large testing sets.