Improving ANN Performance for Imbalanced Data Sets by Means of the NTIL Technique

被引：0
|
作者：
Vivaracho-Pascual, Carlos [1 ]
Simon-Hurtado, Arancha [1 ]
机构：
[1] Univ Valladolid, Dept Comp Sci, E-47002 Valladolid, Spain
来源：
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年
关键词：
RECOGNITION; CLASSIFIER;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper deals with the problem of training an Artificial Neural Network (ANN) when the data sets are very imbalanced. Most learning algorithms, including ANN, are designed for well-balanced data and do not work properly on imbalanced ones. Of the approaches proposed for dealing with this problem, we are interested in the re-sampling ones, since they are algorithm-independent. We have recently proposed a new under-sampling technique for the two-class problem, called Non-Target Incremental Learning (NTIL), which has shown a good performance with SVM, improving results and training speed. Here, the advantages of using this technique with ANN are shown. The performance with regard to other popular under-sampling techniques is compared.
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收藏
页数：6
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