Resampling with neighbourhood bias on imbalanced domains

[1]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[2]  Francisco Herrera,et al.  An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics , 2013, Inf. Sci..

[3]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics (e1071), TU Wien , 2014 .

[4]  William N. Venables,et al.  Modern Applied Statistics with S , 2010 .

[5]  Yunqian Ma,et al.  Imbalanced Learning: Foundations, Algorithms, and Applications , 2013 .

[6]  Luís Torgo,et al.  UBL: an R package for Utility-based Learning , 2016, ArXiv.

[7]  Luís Torgo,et al.  Exploring Resampling with Neighborhood Bias on Imbalanced Regression Problems , 2017, EPIA.

[8]  M. Friedman.  The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[9]  Luís Torgo,et al.  Resampling strategies for regression , 2015, Expert Syst. J. Knowl. Eng..

[10]  Paula Branco.  Re-sampling Approaches for Regression Tasks under Imbalanced Domains , 2014 .

[11]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[12]  Luís Torgo,et al.  SMOTE for Regression , 2013, EPIA.

[13]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[14]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[15]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.