Online Learning With Adaptive Rebalancing in Nonstationary Environments

TL;DR

This paper introduces AREBA, an adaptive online learning algorithm that effectively handles nonstationary and imbalanced data by maintaining class balance, demonstrating superior performance over existing methods in diverse scenarios.

Contribution

The paper presents a novel adaptive rebalancing algorithm for online learning in nonstationary, imbalanced environments, with extensive experimental validation.

Findings

AREBA outperforms state-of-the-art algorithms in learning speed.

AREBA achieves higher learning quality in various class imbalance and concept drift scenarios.

The method is effective on both synthetic and real-world datasets.

Abstract

An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.