Regional Concept Drift Detection and Density Synchronized Drift Adaptation

Regional Concept Drift Detection and Density Synchronized Drift Adaptation

Anjin Liu, Yiliao Song, Guangquan Zhang, Jie Lu

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2280-2286. https://doi.org/10.24963/ijcai.2017/317

In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing data and upcoming data. Since concept drift was first proposed, numerous articles have been published to address this issue in terms of distribution analysis. However, most distribution-based drift detection methods assume that a drift happens at an exact time point, and the data arrived before that time point is considered not important. Thus, if a drift only occurs in a small region of the entire feature space, the other non-drifted regions may also be suspended, thereby reducing the learning efficiency of models. To retrieve non-drifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes. Instead of suspending all historical data after a drift, we synchronize the regional density discrepancies according to LDD. Experimental evaluations on three public data sets show that our concept drift adaptation algorithm improves accuracy compared to other methods.
Keywords:
Machine Learning: Classification
Machine Learning: Online Learning
Machine Learning: Time-series/Data Streams