FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning

10.1109/ICHIS.2005.51

10.1109/TFUZZ.2003.819843

chen, 2007, fuzzy support vector machines for emg pattern recognition and mycroelectrical prosthesis control, Proc 4th Int Symp Neural Netw, 1291

10.1049/ic.2005.0706

10.1109/FSKD.2007.337

10.1109/TFUZZ.2005.859320

10.1109/TFUZZ.2003.817839

10.1109/NAFIPS.2003.1226801

inoue, 2001, fuzzy support vector machine for pattern classification, Proc Int Conf Neural Netw, 1449

10.1007/11539902_151

10.1080/01621459.1961.10482090

10.1016/j.ijar.2008.11.004

10.1007/978-3-540-87479-9_34

10.1007/978-3-642-02478-8_37

10.1007/BF00994018

10.1007/978-1-4757-2440-0

chawla, 2002, smote: synthetic minority over-sampling technique, J Artif Intell Res, 16, 321, 10.1613/jair.953

10.1145/1007730.1007737

zhang, 0, knn approach to unbalanced data distributions: a case study involving information extraction, Proc Int Conf Mach Learning Workshop Learning Imbalanced Data Sets, 42

chawla, 2003, smoteboost: improving prediction of the minority class in boosting, Proc 7th Eur Conf Principles Pract Knowl Discov Databases, 107

liu, 2006, exploratory under sampling for class imbalance learning, Proc IEEE Int Conf Data Mining, 965

10.1109/TKDE.2006.17

10.1145/1007730.1007736

10.1007/s10700-006-7336-8

asuncion, 2007, UCI Repository of Machine Learning Database

10.1080/03610928008827904

10.1214/aoms/1177731944

10.2307/2279372

demsar, 2006, statistical comparisons of classifiers over multiple data sets, J Mach Learning Res, 7, 1

kubat, 1997, addressing the curse of imbalanced training sets: one-sided selection, Proc 14th Int Conf Mach Learning, 179

chang, 2001

batuwita, 2008, an improved non-comparative classification method for human mirna gene prediction, Proc 8th IEEE Int Conf Bioinf Bioneng, 1

10.1093/bioinformatics/btp107

10.1109/ICYCS.2008.72

10.1007/978-3-642-03348-3_54

10.1016/j.fss.2007.04.014

kang, 2006, eus svms: ensemble of under sampled svms for data imbalance problems, Proc Int Conf Neural Inf Process, 837

liu, 2006, boosting prediction accuracy on imbalanced data sets with svm ensembles, Proc 10th Pac -Asia Conf Adv Knowl Discov Data Mining, 107, 10.1007/11731139_15

10.1109/TKDE.2008.239

10.1145/130385.130401

zhang, 1999, using class centres vectors to build support vector machines, Proc IEEE Signal Process Soc Workshop, 3

10.1109/72.991432

10.1145/1007730.1007734

10.1145/1007730.1007733

veropoulos, 1999, controlling the sensitivity of support vector machines, Proc Int Joint Conf Artif Intell, 55

showe-taylor, 2000, Support Vector Machines and Other Kernel-Based Learning Methods

wu, 2003, class-boundary alignment for imbalanced data set learning, presented at the Int Conf Data Mining Workshop Learning Imbalanced Data Sets II

akbani, 2004, applying support vector machines to imbalanced datasets, Proc 15th Eur Conf Mach Learning, 39

10.1145/1007730.1007739

10.1109/TKDE.2005.95

10.1007/11759966_139

10.1007/11941439_30

10.1016/j.patrec.2004.06.009

10.1109/FUZZ.2001.1007345

10.1109/ICMLC.2008.4620507

xiong, 2005, fuzzy support vector machines based on <formula formulatype="inline"><tex notation="tex">$\lambda$</tex></formula>-cut, Proc Int Conf Nat Comput, 292

10.1109/21.256541

ross, 2004, Fuzzy Logic with Engineering Applications

10.1016/0165-0114(89)90245-5

10.1016/0165-0114(94)00282-C