Drift-Aware Multi-Memory Model for Imbalanced Data Streams

TL;DR

This paper introduces DAM3, a novel online learning model that effectively handles class imbalance and concept drift by using a multi-memory approach to preserve old information and adapt to new data.

Contribution

DAM3 is the first model to integrate an imbalance-sensitive drift detector with a multi-memory system for online class imbalance learning.

Findings

DAM3 outperforms state-of-the-art methods on real-world datasets.

The model effectively mitigates class imbalance in streaming data.

DAM3 reduces retroactive interference and preserves minority class information.

Abstract

Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance. Online learning tries to find a trade-off between exploiting previously learned information and incorporating new information into the model. This requires both the incremental update of the model and the ability to unlearn outdated information. The improper use of unlearning, however, can lead to the retroactive interference problem, a phenomenon that occurs when newly learned information interferes with the old information and impedes the recall of previously learned information. The problem becomes more severe when the classes are not equally represented, resulting in the removal of minority information from the model. In this work, we propose the Drift-Aware Multi-Memory Model (DAM3), which addresses the class imbalance problem in online learning for memory-based models. DAM3 mitigates class imbalance by incorporating an imbalance-sensitive drift detector, preserving a balanced representation of classes in the model, and resolving retroactive interference using a working memory that prevents the forgetting of old information. We show through experiments on real-world and synthetic datasets that the proposed method mitigates class imbalance and outperforms the state-of-the-art methods.