Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
| Hi-index | 0.00 |
Correctly detecting the position where a concept begins to drift is important in mining data streams. In this paper, we propose a new method for detecting concept drift. The proposed method, which can detect different types of drift, is based on processing data chunk by chunk and measuring differences between two consecutive batches, as drift indicator. In order to evaluate the proposed method we measure its performance on a set of artificial datasets with different levels of severity and speed of drift. The experimental results show that the proposed method is capable to detect drifts and can approximately find concept drift locations.