DocumentCode :
2749396
Title :
Pruning support vectors for imbalanced data classification
Author :
Chen, Xue-wen ; Gerlach, Byron ; Casasent, David
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Kansas Univ., Lawrence, KS, USA
Volume :
3
fYear :
2005
fDate :
31 July-4 Aug. 2005
Firstpage :
1883
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; backward pruning; class imbalance problem; imbalanced data classification; machine learning; support vector machine classifier; Application software; Costs; Data engineering; Fault detection; Learning systems; Machine learning; Radar detection; Sampling methods; Support vector machine classification; Support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
Type :
conf
DOI :
10.1109/IJCNN.2005.1556167
Filename :
1556167
Link To Document :
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