Abstract
Imbalanced dataset could be found in many real-world domains of applications, e.g. fraud detection problem, threat detection, etc.,. There are various methods that have been proposed in the past to handle the imbalanced data classification problems, but there is still a lack of in-depth research dealing with the case of extremely imbalanced data. Moreover, we can find practically the explosion of imbalance issues within the research of big data analysis, in which the imbalance ratio increases uncontrollably. Based on these two facts, we propose in this study a simplified combination of under-sampling and ensemble learning which can adapt well with different scenarios of extreme imbalance. Experimentally, we conduct our test on 11 datasets, taken from the UCI repository and Kaggle, and can show that our proposed method is not only competitive with two common methods of Under Bagging (UB) and RUSBoost, but also very effective especially in the case of extremely imbalanced big data classification problems.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
