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An Effective Concept Drift Detection Method on Streaming Data Using Probability Estimates
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  • Journal title : Journal of KIISE
  • Volume 43, Issue 6,  2016, pp.718-723
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.6.718
 Title & Authors
An Effective Concept Drift Detection Method on Streaming Data Using Probability Estimates
Kim, Young-In; Park, Cheong Hee;
In streaming data analysis, detecting concept drift accurately is important to maintain the performance of classification model. Error rates are usually used for concept drift detection. However, by describing prediction results with only binary values of 0 or 1, useful information about a behavior pattern of a classifier can be lost. In this paper, we propose an effective concept drift detection method which describes performance pattern of a classifier by utilizing probability estimates for class prediction and detects a significant change in a classifier behavior. Experimental results on synthetic and real streaming data show the efficiency of the proposed method for detecting the occurrence of concept drift.
concept drift detection;probability estimates;streaming data;adaptive incremental learning;
 Cited by
스트리밍 데이터에서의 기계학습,박정희;

한국멀티미디어학회지, 2016. vol.20. 3, pp.1-7
컨셉 변동 스트리밍 데이터를 위한 적응적 가중치 조정을 이용한 동적 앙상블 방법,김영덕;박정희;

정보과학회 논문지, 2017. vol.44. 8, pp.842-853 crossref(new window)
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