# Concept Drift Adaptation by Exploiting Historical Knowledge

arXiv: https://arxiv.org/abs/1702.03500 · HTML: https://mlsanity.com/paper/1702.03500/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2017.

## Built on by (most cited first, 17 of 17)

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- [A Scalable Approach to Covariate and Concept Drift Management via Adaptive Data Segmentation](https://mlsanity.com/paper/2411.15616/) (2024)
- [RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget](https://mlsanity.com/paper/2505.24149/) (2025)
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- [Mixture of Online and Offline Experts for Non-stationary Time Series](https://mlsanity.com/paper/2202.05996/) (2022)
- [MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments](https://mlsanity.com/paper/2509.08176/) (2025)
- [Multi-Label Transfer Learning in Non-Stationary Data Streams](https://mlsanity.com/paper/2509.08181/) (2025)
- [An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity](https://mlsanity.com/paper/2511.02452/) (2025)
- [Keep Rehearsing and Refining: Lifelong Learning Vehicle Routing under Continually Drifting Tasks](https://mlsanity.com/paper/2601.22509/) (2026)
- [NOMADD: Numerical Optimization of Models Adapting to Data Drift](https://mlsanity.com/paper/2608.02845/) (2026)

## Abstract (arXiv)

Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data. Two design questions need to be addressed in developing ensemble methods for incremental learning with concept drift, i.e., which historical (i.e., previously trained) models should be preserved and how to utilize them. A novel ensemble learning method, namely Diversity and Transfer based Ensemble Learning (DTEL), is proposed in this paper. Given newly arrived data, DTEL uses each preserved historical model as an initial model and further trains it with the new data via transfer learning. Furthermore, DTEL preserves a diverse set of historical models, rather than a set of historical models that are merely accurate in terms of classification accuracy. Empirical studies on 15 synthetic data streams and 4 real-world data streams (all with concept drifts) demonstrate that DTEL can handle concept drift more effectively than 4 other state-of-the-art methods.