Pruning support vectors for imbalanced data classification

In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper presents a new m...

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Bibliographic Details
Published inProceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005 Vol. 3; pp. 1883 - 1888 vol. 3
Main Authors Xue-wen Chen, Gerlach, B., Casasent, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Application software
Costs
Data engineering
Fault detection
Learning systems
Machine learning
Radar detection
Sampling methods
Support vector machine classification
Support vector machines
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Summary:In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper presents a new method for imbalanced data classification. The proposed method is based on support vector machine classifiers and backward pruning technique. The experimental results obtained on two data sets demonstrate the effectiveness of the new algorithm.
ISBN:0780390482
9780780390485
ISSN:2161-4393
DOI:10.1109/IJCNN.2005.1556167