Imbalanced Data Classification Using Weighted Voting Ensemble.

Imbalanced data classification is still remaining thje important topic and during the past decades, plenty of works are devoted to this field of study. More and more real-life based imbalanced class problems inspired researchers to come up with new solutions with better performance. Various techniques are employed such as data handling approaches, algorithm-level approaches, active learning approaches, and kernel-based methods to enumerate only a few. This work aims at applying a novel dynamic selection methods on imbalanced data classification problems. The experiments carried out on several benchmark datasets confirm its pretty high performance.