On the Advantages of Weighted L1-Norm Support Vector Learning for Unbalanced Binary Classification Problems

Author

Eitrich, Tatjana ; Lang, Bruno

Author_Institution

Central Inst. for Appl. Math., Res. Centre Juelich

fYear

2006

fDate

Sept. 2006

Firstpage

575

Lastpage

580

Abstract

In this paper we analyze support vector machine classification using the soft margin approach that allows for errors and margin violations during the training stage. Two models for learning the separating hyperplane do exist. We study the behavior of the optimization algorithms in terms of training characteristics and test accuracy for unbalanced data sets. The main goal of our work is to compare the features of the resulting classification functions, which are mainly defined by the support vectors arising during the support vector machine training

Keywords

learning (artificial intelligence); optimisation; pattern classification; support vector machines; classification function; optimization algorithm; soft margin approach; support vector learning; support vector machine classification; support vector machine training; unbalanced binary classification problem; unbalanced data set; Intelligent systems; Kernel; Learning systems; Machine learning; Machine learning algorithms; Mathematics; Supervised learning; Support vector machine classification; Support vector machines; Testing; Soft Margin Algorithms; Supervised Learning; Support Vector Machine Classification; Unbalanced Data;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2006 3rd International IEEE Conference on

Conference_Location

London

Print_ISBN

1-4244-01996-8

Electronic_ISBN

1-4244-01996-8

Type

conf

DOI

10.1109/IS.2006.348483

Filename

4155490