Concept Drift Adaptation by Exploiting Historical Knowledge
This addresses the problem of maintaining model accuracy over time for applications with evolving data streams, though it is incremental as it builds on existing ensemble approaches.
The paper tackles concept drift adaptation in incremental learning by proposing DTEL, an ensemble method that preserves diverse historical models and retrains them via transfer learning on new data, achieving more effective drift handling than four state-of-the-art methods on synthetic and real-world data streams.
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.