Document Type

Conference Paper


Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence


Computer Sciences, Information Science

Publication Details

The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems (HaCDAIS) which was organized in conjunction with the 11th IEEE International Conference on Data Mining (IEEE ICDM 2011) Vancouver, Canada,11-14, December .


Concept drift is believed to be prevalent inmost data gathered from naturally occurring processes andthus warrants research by the machine learning community.There are a myriad of approaches to concept drift handlingwhich have been shown to handle concept drift with varyingdegrees of success.

However, most approaches make the keyassumption that the labelled data will be available at nolabelling cost shortly after classification, an assumption whichis often violated. The high labelling cost in many domainsprovides a strong motivation to reduce the number of labelledinstances required to handle concept drift. Explicit detectionapproaches that do not require labelled instances to detectconcept drift show great promise for achieving this.

Ourapproach Confidence Distribution Batch Detection (CDBD)provides a signal correlated to changes in concept without usinglabelled data. We also show how this signal combined with atrigger and a rebuild policy can maintain classifier accuracywhile using a limited amount of labelled data.