Treffer: Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Title:
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
Authors:
Source:
Mathematical and computer modelling. 54(3-4):1093-1099
Publisher Information:
Kidlington: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 24 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Méthodes de calcul scientifique (y compris calcul symbolique, calcul algébrique), Methods of scientific computing (including symbolic computation, algebraic computation), Analyse assistée, Computer aided analysis, Análisis asistido, Analyse numérique, Numerical analysis, Análisis numérico, Apprentissage, Learning, Aprendizaje, Calcul variationnel, Variational calculus, Cálculo de variaciones, Classification, Clasificación, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Modèle mathématique, Mathematical model, Modelo matemático, Méthode optimisation, Optimization method, Método optimización, Performance algorithme, Algorithm performance, Resultado algoritmo, Programmation mathématique, Mathematical programming, Programación matemática, Résultat expérimental, Experimental result, Resultado experimental, 49XX, 65K10, 65Kxx, Imbalanced data learning, Kernel optimization, Support vector machine, Weighted maximum margin criterion
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
College of Science, China Agricultural University, Beijing, 100083, China
ISSN:
0895-7177
Rights:
Copyright 2015 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
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:
Mathematics
Accession Number:
edscal.24266380
Database:
PASCAL Archive
Weitere Informationen
As a kernel-based method, whether the selected kernel matches the data determines the performance of support vector machine. Conventional support vector classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. In this paper, we propose a weighted maximum margin criterion to optimize the data-dependent kernel, which makes the minority class more clustered in the induced feature space. We train support vector classification with the optimal kernel. The experimental results on nine benchmark data sets indicate the effectiveness of the proposed algorithm for imbalanced data classification problems.