A Hybrid Evolutionary Under-sampling Method for Handling the Class Imbalance Problem with Overlap in Credit Classification

This publication has 51 references indexed in Scilit:

• Improving classification rate constrained to imbalanced data between overlapped and non-overlapped regions by hybrid algorithms
  Neurocomputing, 2015
• SMOTE–IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering
  Information Sciences, 2015
• Genetic algorithm-based heuristic for feature selection in credit risk assessment
  Expert Systems with Applications, 2014
• One-class classification: taxonomy of study and review of techniques
  The Knowledge Engineering Review, 2014
• EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
  Pattern Recognition, 2013
• An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
  Information Sciences, 2013
• Instance sampling in credit scoring: An empirical study of sample size and balancing
  International Journal of Forecasting, 2012
• Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
  Soft Computing, 2010
• Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy
  Evolutionary Computation, 2009
• A study of the behavior of several methods for balancing machine learning training data
  ACM SIGKDD Explorations Newsletter, 2004