The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our method, a MSL learner is structured in a recursive manner, which contains all the base learners maintained in a hierarchy, ensuring the learned concepts are traceable. We evaluated MSL and two ensemble techniques on three synthetic datasets, which contain a number of drastic concept drifts. The experimental results show that the proposed method generally performs better than the ensemble techniques in terms of prediction accuracy.
|Name||KDIR 2010 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval|
|Conference||International Conference on Knowledge Discovery and Information Retrieval, KDIR 2010|
|Period||25/10/2010 → 28/10/2010|
- Concept drift
- Data mining
- Evolving data
- Supervised learning