Rebalancing Learning on Evolving Data Streams

TL;DR

This paper introduces a novel online rebalancing method for evolving data streams, addressing the challenge of class imbalance in unbounded, changing data environments, and demonstrates its effectiveness through synthetic data experiments.

Contribution

It proposes the first incremental rebalancing technique for data streams, improving learning performance on imbalanced, evolving data.

Findings

Outperforms existing rebalancing methods in synthetic data tests

Effective in managing class imbalance in streaming data

Enhances model accuracy on evolving data streams

Abstract

Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine learning techniques are not able to deal with data whose statistics is subject to gradual or sudden changes without any warning. Massive Online Analysis (MOA) is the collective name, as well as a software library, for new learners that are able to manage data streams. In this paper, we present a research study on streaming rebalancing. Indeed, data streams can be imbalanced as static data, but there is not a method to rebalance them incrementally, one element at a time. For this reason we propose a new streaming approach able to rebalance data streams online. Our new methodology is evaluated against some synthetically generated datasets using prequential evaluation in order to demonstrate that it outperforms the existing approaches.