  Machine learning classifiers often stumble over imbalanced datasets where
classes are not equally represented. This inherent bias towards the majority
class may result in low accuracy in labeling minority class. Imbalanced
learning is prevalent in many real-world applications, such as medical
research, network intrusion detection, and fraud detection in credit card
transactions, etc. A good number of research works have been reported to tackle
this challenging problem. For example, Synthetic Minority Over-sampling
TEchnique (SMOTE) and ADAptive SYNthetic sampling approach (ADASYN) use
oversampling techniques to balance the skewed datasets. In this paper, we
propose a novel method that combines a Weighted Oversampling Technique and
ensemble Boosting method (WOTBoost) to improve the classification accuracy of
minority data without sacrificing the accuracy of the majority class. WOTBoost
adjusts its oversampling strategy at each round of boosting to synthesize more
targeted minority data samples. The adjustment is enforced using a weighted
distribution. We compare WOTBoost with other four classification models (i.e.,
decision tree, SMOTE + decision tree, ADASYN + decision tree, SMOTEBoost)
extensively on 18 public accessible imbalanced datasets. WOTBoost achieves the
best G mean on 6 datasets and highest AUC score on 7 datasets.
