Resampling-based ensemble methods for online class imbalance learning

Research output: Contribution to journalArticlepeer-review

Colleges, School and Institutes

Abstract

Online class imbalance learning is a new learning problem that combines the challenges of both online learning and class imbalance learning. It deals with data streams having very skewed class distributions. This type of problems commonly exists in real-world applications, such as fault diagnosis of real-time control monitoring systems and intrusion detection in computer networks. In our earlier work, we defined class imbalance online, and proposed two learning algorithms OOB and UOB that build an ensemble model overcoming class imbalance in real time through resampling and time-decayed metrics. In this paper, we further improve the resampling strategy inside OOB and UOB, and look into their performance in both static and dynamic data streams.We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. We find that UOB is better at recognizing minority-class examples in static data streams, and OOB is more robust against dynamic changes in class imbalance status. The data distribution is a major factor affecting their performance. Based on the insight gained, we then propose two new ensemble methods that maintain both OOB and UOB with adaptive weights for final predictions, called WEOB1 and WEOB2. They are shown to possess the strength of OOB and UOB with good accuracy and robustness.

Details

Original languageEnglish
Pages (from-to)1356 - 1368
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number5
Early online date5 Aug 2014
Publication statusPublished - 1 May 2015

Keywords

  • Class imbalance, resampling, online learning, ensemble learning, Bagging