Abstract
The problem of data imbalance have attract a lot of attentions of the researchers, and them of machine learning and data mining recognized this as a key factor in data classification. Ensemble classification is a excellent method that used in machine learning and has demonstrated promising capabilities in improving classification accuracy. And Support vector machines ensemble has been proposed to improve classification performance recently. In this paper we used the fuzzy integral technique in SVM ensemble to evaluate the output of SVM in imbalanced data. And we compared this method with SVM, neural network and the LDA. The results indicate that the proposed method has better classification performance than others.
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Chen, P., Zhang, D. (2014). Constructing Support Vector Machines Ensemble Classification Method for Imbalanced Datasets Based on Fuzzy Integral. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_8
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DOI: https://doi.org/10.1007/978-3-319-07455-9_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07454-2
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