Diversity of Ensembles for Data Stream Classification

TL;DR

This paper analyzes how diversity measures influence the success of ensemble classifiers in streaming data with concept drift, providing insights into their applicability and effectiveness in evolving environments.

Contribution

It offers a theoretical analysis of diversity measures in the context of streaming data ensembles, highlighting their relevance and limitations for online learning.

Findings

Diversity measures impact ensemble performance in data streams.

Certain diversity measures are more applicable to streaming data.

The analysis clarifies the role of diversity in handling concept drift.

Abstract

When constructing a classifier ensemble, diversity among the base classifiers is one of the important characteristics. Several studies have been made in the context of standard static data, in particular, when analyzing the relationship between a high ensemble predictive performance and the diversity of its components. Besides, ensembles of learning machines have been performed to learn in the presence of concept drift and adapt to it. However, diversity measures have not received much research interest in evolving data streams. Only a few researchers directly consider promoting diversity while constructing an ensemble or rebuilding them in the moment of detecting drifts. In this paper, we present a theoretical analysis of different diversity measures and relate them to the success of ensemble learning algorithms for streaming data. The analysis provides a deeper understanding of the concept of diversity and its impact on online ensemble Learning in the presence of concept drift. More precisely, we are interested in answering the following research question; Which commonly used diversity measures are used in the context of static-data ensembles and how far are they applicable in the context of streaming data ensembles?