Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift

TL;DR

This paper introduces a novel ensemble machine learning approach using genetic algorithms to adaptively handle concept drift in dynamic environments, outperforming single models especially with unknown drift characteristics.

Contribution

The study presents a new two-level ensemble ML model combined with GAs for adaptive concept drift detection and re-training, enhancing model robustness in changing data environments.

Findings

Outperforms single ML models with CD algorithms in synthetic datasets.

Utilizes off-the-shelf AutoML methods to further improve performance.

Effective in scenarios with unknown concept drift characteristics.

Abstract

Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientific and engineering domains. When using ML models in realistic and dynamic environments, users need to often handle the challenge of concept drift (CD). In this study, we explore the application of genetic algorithms (GAs) to address the challenges posed by CD in such settings. We propose a novel two-level ensemble ML model, which combines a global ML model with a CD detector, operating as an aggregator for a population of ML pipeline models, each one with an adjusted CD detector by itself responsible for re-training its ML model. In addition, we show one can further improve the proposed model by utilizing off-the-shelf automatic ML methods. Through extensive synthetic dataset analysis, we show that the proposed model outperforms a single ML pipeline with a CD algorithm, particularly in scenarios with unknown CD characteristics. Overall, this study highlights the potential of ensemble ML and CD models obtained through a heuristic and adaptive optimization process such as the GA one to handle complex CD events.