Employing chunk size adaptation to overcome concept drift

TL;DR

This paper introduces a Chunk Adaptive Restoration framework that dynamically adjusts data chunk sizes in ensemble classifiers to improve responsiveness to concept drift in streaming data, enhancing model adaptation speed.

Contribution

It proposes a novel framework for adaptive chunk sizing that can be integrated with existing block-based classifiers to better handle concept drift.

Findings

Significantly reduces model restoration time

Improves classifier responsiveness to concept drift

Validated through experimental and statistical analysis

Abstract

Modern analytical systems must be ready to process streaming data and correctly respond to data distribution changes. The phenomenon of changes in data distributions is called concept drift, and it may harm the quality of the used models. Additionally, the possibility of concept drift appearance causes that the used algorithms must be ready for the continuous adaptation of the model to the changing data distributions. This work focuses on non-stationary data stream classification, where a classifier ensemble is used. To keep the ensemble model up to date, the new base classifiers are trained on the incoming data blocks and added to the ensemble while, at the same time, outdated models are removed from the ensemble. One of the problems with this type of model is the fast reaction to changes in data distributions. We propose a new Chunk Adaptive Restoration framework that can be adapted to any block-based data stream classification algorithm. The proposed algorithm adjusts the data chunk size in the case of concept drift detection to minimize the impact of the change on the predictive performance of the used model. The conducted experimental research, backed up with the statistical tests, has proven that Chunk Adaptive Restoration significantly reduces the model's restoration time.