Entropy-based fuzzy support vector machine for imbalanced datasets
Tóm tắt
Từ khóa
Tài liệu tham khảo
Akbani, 2004, Applying support vector machines to imbalanced datasets, 39
Alcalá-Fdez, 2011, KEEL: a software tool to assess evolutionary algorithms for data mining problems, J. Multiple-Valued Logic Soft Comput., 17, 2
Batista, 2004, A study of the behavior of several methods for balancing machine learning training data, 1, 20
Batuwita, 2010, FSVM-CIL: fuzzy support vector machines for class imbalance learning, IEEE Trans. Fuzzy Syst., 18, 558, 10.1109/TFUZZ.2010.2042721
Bowyer, 2002, SMOTE: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16, 321, 10.1613/jair.953
Braga-Neto, 2004, Is cross-validation valid for small-sample microarray classification?, Bioinformatics, 20, 374, 10.1093/bioinformatics/btg419
Breiman, 1984, Classification and regression trees
Brown, 2012, An experimental comparison of classification algorithms for imbalanced credit scoring data sets, Expert Syst. Appl., 39, 3446, 10.1016/j.eswa.2011.09.033
Bunkhumpornpat, 2009, Safe-level-SMOTE: safe-level-synthetic minority over-sampling TEchnique for handling the class imbalanced problem, 475
Chaudhuri, 2011, Fuzzy support vector machine for bankruptcy prediction, Appl. Soft Comput., 11, 2472, 10.1016/j.asoc.2010.10.003
Chawla, 2008, Automatically countering imbalance and its empirical relationship to cost, Data Min. Knowl. Discov., 17, 225, 10.1007/s10618-008-0087-0
Chen, 2010, FRSVMs: fuzzy rough set based support vector machines, Fuzzy Sets Syst., 161, 596, 10.1016/j.fss.2009.04.007
Chen, 2014, An entropy-based uncertainty measurement approach in neighborhood systems, Inf Sci (Ny), 279, 239, 10.1016/j.ins.2014.03.117
Dai, 2015, Class imbalance learning via a fuzzy total margin based support vector machine, Appl. Soft. Comput., 31, 172, 10.1016/j.asoc.2015.02.025
Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1
Deng, 2013, Nonlinear process fault pattern recognition using statistics kernel PCA similarity factor, Neurocomputing, 121, 298, 10.1016/j.neucom.2013.04.042
Galar, 2012, A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42, 463, 10.1109/TSMCC.2011.2161285
Galar, 2013, Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling, Pattern Recognit., 46, 3460, 10.1016/j.patcog.2013.05.006
García, 2012, Evolutionary-based selection of generalized instances for imbalanced classification, Knowl. Based Syst., 25, 3, 10.1016/j.knosys.2011.01.012
García, 2008, K-NN performance in a challenging scenario of imbalance and overlapping, Pattern Anal. Appl., 11, 269, 10.1007/s10044-007-0087-5
García-Pedrajas, 2013, OligoIs: scalable instance selection for class-imbalanced data sets, IEEE Trans. Cybern., 43, 332, 10.1109/TSMCB.2012.2206381
Guo, 2014, Oil spill detection using synthetic aperture radar images and feature selection in shape space, Int. J. Appl. Earth Obs. Geoinf., 30, 146, 10.1016/j.jag.2014.01.011
Hanmandlu, 2014, A new entropy function and a classifier for thermal face recognition, Eng. Appl. Artif. Intell., 36, 269, 10.1016/j.engappai.2014.06.028
He, 2009, Learning from imbalanced data, IEEE Trans. Knowl. Data Eng., 21, 1263, 10.1109/TKDE.2008.239
Hong, 2007, A kernel-based two-class classifier for imbalanced data sets, IEEE Trans. Neural Netw., 18, 28, 10.1109/TNN.2006.882812
Huang, 2005, Using AUC and accuracy in evaluating learning algorithms, IEEE Trans. Knowl. Data Eng., 17, 299, 10.1109/TKDE.2005.50
Japkowicz, 2002, The class imbalance problem: a systematic study, Intell. Data Anal., 6, 429, 10.3233/IDA-2002-6504
Kubat, 1997, Addressing the curse of imbalanced training sets: one-sided selection, 97, 179
Liu, 2009, Exploratory undersampling for class-imbalance learning, IEEE Trans. Syst. Man Cybern.Part B, 39
Long, 2013, An entropy-based multispectral image classification algorithm, IEEE Trans. Geosci. Remote Sens., 51, 5225, 10.1109/TGRS.2013.2272560
Maldonado, 2014, Imbalanced data classification using second-order cone programming support vector machines, Pattern Recognit., 47, 2070, 10.1016/j.patcog.2013.11.021
Newby, 2013, Coping with unbalanced class data sets in oral absorption models, J. Chem. Inf. Model., 53, 461, 10.1021/ci300348u
Ozcift, 2011, Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms, Comput. Methods Programs Biomed., 104, 443, 10.1016/j.cmpb.2011.03.018
Platt, 1999, Fast training of support vector machines using sequential minimal optimization, Adv. Kernel Methods, 3, 185
Rahman, 2014, A novel machine learning approach toward quality assessment of sensor data, IEEE Sens. J., 14, 1035, 10.1109/JSEN.2013.2291855
Raskutti, 2004, Extreme re-balancing for SVMs: a case study, SIGKDD Explor. Newsl., 6, 60, 10.1145/1007730.1007739
Renyi, 1970
Shannon, 2001, A mathematical theory of communication, SIGMOBILE Mobile Comput. Commun.Rev., 5, 3, 10.1145/584091.584093
Sonka, 2014
Sun, 2009, Classification of imbalanced data: a review, Int. J. Pattern Recognit. Artif. Intell., 23, 687, 10.1142/S0218001409007326
Tapkan, 2016, A cost-sensitive classification algorithm: BEE-miner, Knowl. Based Syst., 95, 99, 10.1016/j.knosys.2015.12.010
Tian, 2012, Fuzzy support vector machine based on non-equilibrium data, 2, 448
Tomek, 1976, Two modifications of CNN, IEEE Trans. Syst. Man Commun., 6, 769, 10.1109/TSMC.1976.4309452
Tsang, 2006, Efficient kernel feature extraction for massive data sets
Vapnik, 2000
Wang, 2016, Efficient graph similarity join for information integration on graphs, Front. Comput. Sci., 10, 317, 10.1007/s11704-015-4505-3
Wang, 2005, A new fuzzy support vector machine to evaluate credit risk, IEEE Trans. Fuzzy Syst., 13, 820, 10.1109/TFUZZ.2005.859320
Yang, 2011, A kernel fuzzy C-means clustering based fuzzy support vector machine algorithm for classification problems with outliers or noises,, IEEE Trans. Fuzzy Syst., 19, 105, 10.1109/TFUZZ.2010.2087382
Zhang, 2008, A rough margin based support vector machine, Inf. Sci., 178, 2204, 10.1016/j.ins.2007.12.012
Zhang, 2016, Multi-view dimensionality reduction via canonical random correlation analysis, Front. Comput. Sci., 10, 856, 10.1007/s11704-015-4538-7
Zhao, 2015, Cost-sensitive online classification with adaptive regularization and its applications, 649