Papers

Papers

1. Papers: A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches

   • An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
     • An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
     • Diversity techniques improve the performance of the best imbalance learning ensembles
     • ROSEFW-RF: The winner algorithm for the ECBDL’14 big data competition: An extremely imbalanced big data bioinformatics problem
     • Neighbourhood sampling in bagging for imbalanced data
     • A new approach for imbalanced data classification based on data gravitation
   • Roughly Balanced Bagging for Imbalanced Data
   • Hybrid Sampling with Bagging for Class Imbalance Learning

2. RUSBoost: A Hybrid Approach to Alleviating Class Imbalance