Acknowledgements. This paper is supported by Natural Youth Science Foundation of China
(61501326), the National Natural Science Foundation of China (61731006).
References
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for a class imbalanced data set using genetic programming and support vector machines:
case study for wilt disease classification. Remote Sens Lett 6(7):568–577
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classification. In: 2015 IEEE international symposium on multimedia (ISM), pp 483–488
6. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) DBSMOTE: density-based
synthetic minority over-sampling technique. Appl Intell 36(3):664–684
7. He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for
imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE
world congress on computational intelligence), pp 1322–1328
8. Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in
imbalanced data sets learning. In: International conference on intelligent computing, pp 878–
887
9. Datta S, Das S (2015) Near-Bayesian support vector machines for imbalanced data
classification with equal or unequal misclassification costs. Neural Netw 70:39–52
10. Eeti LN, Buddhiraju KM (2018) Classification of hyperspectral remote sensing Images by an
ensemble of support vector machines under imbalanced data. In: IGARSS 2018-2018 IEEE
international geoscience and remote sensing symposium, pp 2659–2661
11. Wiering MA, Schutten M, Millea A, Meijster A, Schomaker LRB (2013) Deep support
vector machines for regression problems. In: International workshop on advances in
regularization, optimization, kernel methods, and support vector machines, pp 53–54
12. Wiering MA, Schomaker LRB (2014) Multi-layer support vector machines. In: Regularization optimization kernels and support vector machines. CRC Press, Boca Raton, pp 457–
475
Imbalanced Data Classification with Deep Support Vector Machines
95
(61501326), the National Natural Science Foundation of China (61731006).
References
1. Al Najada H, Zhu X (2014) ISRD: spam review detection with imbalanced data
distributions. In: IEEE international conference on information reuse and integration,
Redwood City, CA, USA, pp 553–560
2. Hassan AK, Abraham A (2016) Modeling insurance fraud detection using imbalanced data
classification. In: Advances in nature and biologically inspired computing, pp 117–127
3. Vorobeva A (2016) Examining the performance of classification algorithms for imbalanced
data sets in web author identification. In: Proceedings of the 18th conference of open
innovations association FRUCT, pp 385–390
4. Mohd Pozi MS, Sulaiman MN, Mustapha N, Perumal T (2015) A new classification model
for a class imbalanced data set using genetic programming and support vector machines:
case study for wilt disease classification. Remote Sens Lett 6(7):568–577
5. Yan Y, Chen M, Shyu ML, Chen SC (2015) Deep learning for imbalanced multimedia data
classification. In: 2015 IEEE international symposium on multimedia (ISM), pp 483–488
6. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) DBSMOTE: density-based
synthetic minority over-sampling technique. Appl Intell 36(3):664–684
7. He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for
imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE
world congress on computational intelligence), pp 1322–1328
8. Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in
imbalanced data sets learning. In: International conference on intelligent computing, pp 878–
887
9. Datta S, Das S (2015) Near-Bayesian support vector machines for imbalanced data
classification with equal or unequal misclassification costs. Neural Netw 70:39–52
10. Eeti LN, Buddhiraju KM (2018) Classification of hyperspectral remote sensing Images by an
ensemble of support vector machines under imbalanced data. In: IGARSS 2018-2018 IEEE
international geoscience and remote sensing symposium, pp 2659–2661
11. Wiering MA, Schutten M, Millea A, Meijster A, Schomaker LRB (2013) Deep support
vector machines for regression problems. In: International workshop on advances in
regularization, optimization, kernel methods, and support vector machines, pp 53–54
12. Wiering MA, Schomaker LRB (2014) Multi-layer support vector machines. In: Regularization optimization kernels and support vector machines. CRC Press, Boca Raton, pp 457–
475
Imbalanced Data Classification with Deep Support Vector Machines
95