Imbalance: Oversampling algorithms for imbalanced classification in R
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Francisco Herrera | Salvador García | Alberto Fernández | Ignacio Cordón | S. García | F. Herrera | Alberto Fernández | Ignacio Cordón | A. Fernández
[1] Ioannis A. Kakadiaris,et al. NEATER: Filtering of Over-sampled Data Using Non-cooperative Game Theory , 2014, ICPR.
[2] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[3] Francisco Herrera,et al. An insight into imbalanced Big Data classification: outcomes and challenges , 2017 .
[4] Nicola Torelli,et al. Training and assessing classification rules with imbalanced data , 2012, Data Mining and Knowledge Discovery.
[5] Gerald Schaefer,et al. Cost-sensitive decision tree ensembles for effective imbalanced classification , 2014, Appl. Soft Comput..
[6] Xin Yao,et al. MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , 2014 .
[7] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[8] Jesús Alcalá-Fdez,et al. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework , 2011, J. Multiple Valued Log. Soft Comput..
[9] Ying Ju,et al. Finding the Best Classification Threshold in Imbalanced Classification , 2016, Big Data Res..
[10] Gianluca Bontempi,et al. Racing for Unbalanced Methods Selection , 2013, IDEAL.
[11] Xindong Wu,et al. Online feature selection for high-dimensional class-imbalanced data , 2017, Knowl. Based Syst..
[12] Nicola Torelli,et al. ROSE: a Package for Binary Imbalanced Learning , 2014, R J..
[14] Bidyut Baran Chaudhuri,et al. Handling data irregularities in classification: Foundations, trends, and future challenges , 2018, Pattern Recognit..
[15] Fernando Nogueira,et al. Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning , 2016, J. Mach. Learn. Res..
[16] Tsuyoshi Murata,et al. {m , 1934, ACML.
[17] Diane J. Cook,et al. RACOG and wRACOG: Two Probabilistic Oversampling Techniques , 2015, IEEE Transactions on Knowledge and Data Engineering.
[18] Sheng Chen,et al. PDFOS: PDF estimation based over-sampling for imbalanced two-class problems , 2014, Neurocomputing.
[19] María José del Jesús,et al. KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining , 2017, Int. J. Comput. Intell. Syst..
[20] Huaxiang Zhang,et al. RWO-Sampling: A random walk over-sampling approach to imbalanced data classification , 2014, Inf. Fusion.
[21] Francisco Herrera,et al. SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary , 2018, J. Artif. Intell. Res..