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http://www.csi.uottawa.ca/~nat/Papers/29-Wang.pdf
Boosting Support Vector Machines for Imbalanced Data Sets Benjamin X. Wang and Nathalie Japkowicz School of information Technology and Engineering, University of Ottawa, 800 King Edward Ave., P.O.Box 450 Stn.A, Ottawa, Ontario, K1N 6N5,Canada fbxwang,[email protected] Abstract Real world data mining applications must address the issue of
https://www.researchgate.net/publication/225509786_Boosting_Support_Vector_Machines_for_Imbalanced_Data_Sets
A boosting algorithm for support vector machine [28] is proposed for countering the excessive bias in classifying imbalance data. FSVM for class imbalance in medical datasets is proposed [29] for ...
https://www.sciencedirect.com/science/article/pii/S1877050918322269
The result has been shown that using only informative features in the classification task produces better classification performance than using all features on imbalanced microarray datasets. References [1] Akbani, R., Kwek, S., & Japkowicz, N. (2004). Applying Support Vector Machines to imbalanced datasets.Cited by: 1
https://link.springer.com/chapter/10.1007%2F978-3-540-68123-6_4
Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of... Boosting Support Vector Machines for Imbalanced Data Sets SpringerLinkCited by: 58
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.6423
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack ...
https://sci2s.ugr.es/keel/pdf/specific/congreso/akbani_svm_2004.pdf
Applying Support Vector Machines to Imbalanced Datasets Rehan Akbani1, Stephen Kwek1, and Nathalie Japkowicz2 1 Department of Computer Science, University of Texas at San Antonio 6900 N. Loop 1604 W, San Antonio, Texas, 78249, USA {rakbani,kwek}@cs.utsa.edu 2 School of Information Technology & Engineering, University of Ottawa
https://pdfs.semanticscholar.org/859b/6368b233ead49ff2c6cb0275123c4830aa19.pdf
AdaBoost and Support Vector Machines for Unbalanced Data Sets Chi Zhang Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA Email: [email protected] Abstract—Boost is a kind of method for improving the accu-racy of a given learning algorithm by combining multiple weak
http://www.cs.ox.ac.uk/people/vasile.palade/papers/Class-Imbalance-SVM.pdf
2 CLASS IMBALANCE LEARNING METHODS FOR SUPPORT VECTOR MACHINES capability and ability to nd global and non-linear classi cation solutions, SVMs have been very popular among the machine learning and data mining researchers. Although SVMs often work e ectively with balanced datasets, they could produce suboptimal results with imbalanced datasets.
https://towardsdatascience.com/support-vector-machines-imbalanced-data-feb3ecffbb0e
Dec 18, 2019 · Observe how SVM works in case of an imbalanced dataset. Observe how the hyperplane changes according to the change in the regularization term. A brief about SVMs. In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis ...
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