|
[1]
|
J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez and F. Herrera, KEEL data-mining software tool: Data set repository, Integration of Algorithms and Experimental Analysis Framework, Journal of Multiple-Valued Logic and Soft Computing, 17 (2011), 255-287.
|
|
[2]
|
A. Asuncion and D. J. Newman,
UCI Repository of Machine Learning Database (School of Information and Computer Science), Irvine, CA: Univ. of California [Online], 2007. Available: http://www.ics.uci.edu/mlearn/MLRepository.html
|
|
[3]
|
I. Brown and C. Mues, An experimental comparison of classification algorithms for imbalanced credit scoring data sets, Expert Systems with Applications, 39 (2012), 3446-3453.
doi: 10.1016/j.eswa.2011.09.033.
|
|
[4]
|
P. Cao, D. Zhao and O. Zaiane, A PSO-based cost-sensitive neural network for imbalanced data classification, Trends and Applications in Knowledge Discovery and Data Mining, (2013), 452-463.
doi: 10.1007/978-3-642-40319-4_39.
|
|
[5]
|
Y. Chen,
Learning Classifiers from Imbalanced Only Positive and Unlabeled Data Sets 2008 UC San Diego Data Mining Contest.
|
|
[6]
|
Y. Chen, S. Tang, L. Zhou, C. Wang, J. Du, T. Wang and S. Pei, Decentralized Clustering by Finding Loose and Distributed Density Cores, Inform. Sci., 433/434 (2018), 510-526.
doi: 10.1016/j.ins.2016.08.009.
|
|
[7]
|
Doucette and M. I. Heywood, Classification under imbalanced data sets:Active sub-sampling and auc approximation, M. O'Neill et al. Eds.:EuroGP 2008, LNCS, 4971 (2008), 266-277.
|
|
[8]
|
B. J. Frey and D. Dueck, Clustering by passing messages between data points, Science, 315 (2007), 972-976.
doi: 10.1126/science.1136800.
|
|
[9]
|
G. Hulten, L. Spencer and P. Domingos, Mining time-changing data streams, In: ACM
SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, (2001), 97-106.
doi: 10.1145/502512.502529.
|
|
[10]
|
A. K. Jain, Data clustering:50 years beyond K-means, Part of the Lecture Notes in Computer Science book series, 5211 (2008), 3-4.
doi: 10.1007/978-3-540-87479-9_3.
|
|
[11]
|
R. Kohavi, Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid, In: Second International Conference on Knoledge Discovery and Data Mining, (1996), 202-207.
|
|
[12]
|
V. López, I. Triguero, C. J. Carmona, S. García and F. Herrera, Addressing imbalanced classification withinstance generation techniques: IPADE-ID, Neurocomputing, 126 (2014), 15-28.
|
|
[13]
|
A. C. Lorena, L. F. O. Jacintho, M. F. Siqueira, R. De Giovanni, L. G. Lohmann, A. C. P. L. F. de Carvalho and M. Yamamoto, Comparing machine learning classifiers in potential distribution modelling, Expert Systems with Applications, 38 (2011), 5268-5275.
doi: 10.1016/j.eswa.2010.10.031.
|
|
[14]
|
H. Ma,
Correlation-based Feature Subset Selection For Machine Learning PhD Thesis, 1998.
|
|
[15]
|
A. K. Menon, H. Narasimhan, S. Agarwal and S. Chawla, On the statistical consistency of algorithms for binary classification under class imbalance, Appearing in Proceedings of the 30 thInternational Conference on Machine Learning Atlanta, Georgia, USA, 2013.
|
|
[16]
|
A. Rodriguez and A. Laio, Clustering by fast search and find of density peaks, Science, 344 (2014), 1492-1496.
doi: 10.1126/science.1242072.
|
|
[17]
|
N. Verbiesta, E. Ramentol, C. Cornelisa and F. Herrera, Preprocessing noisy imbalanced datasets using SMOTE enhanced withfuzzy rough prototype selection, Applied Soft Computing, 22 (2014), 511-517.
|
|
[18]
|
S. Wang, L. L. Minku and X. Yao, Resampling-based ensemble methods for online class imbalance learning, IEEE Transactions on Knowledge and Data Engineering, 27 (2015), 1356-1368.
doi: 10.1109/TKDE.2014.2345380.
|
|
[19]
|
I. H. Witten and E. Frank, Data mining:Practical machine learning tools and techniques, Newsletter: ACM SIGMOD Record Homepage Archive, 31 (2002), 76-77.
doi: 10.1145/507338.507355.
|
|
[20]
|
B. Yang and L. Jing, A Novel nonparallel plane proximal svm for imbalance data classification Journal of Software, 9 2014.
|
Download:
{{journal.titleEn}} 
