TY  - CONF
AB  - Abstract. In online machine learning, the ability to adapt to new concept quick-ly is highly desired. In this paper, we propose a novel concept drift detection method, which is called Anomaly Analysis Drift Detection (AADD), to im-prove the performance of machine learning algorithms under non-stationary en-vironment. The proposed AADD method is based on an anomaly analysis of learner?s accuracy associate with the similarity between learners? training do-main and test data. This method first identifies whether there are conflicts be-tween current concept and new coming data. Then the learner will incremental-ly learn the non-conflict data, which will not decrease the accuracy of the learn-er on previous trained data, for concept extension. Otherwise, a new learner will be created based on the new data. Experiments illustrate that this AADD meth-od can detect new concept quickly and learn extensional drift incrementally.
AU  - Liu, A
AU  - Zhang, G
AU  - Lu, J
DA  - 2014/12/01
JO  - International Conference on Neural Information Processing
PY  - 2014/12/01
TI  - Concept Drift Detection based on Anomaly Analysis
Y1  - 2014/12/01
Y2  - 2026/05/04
ER  -