Modeling the Example Life-Cycle in an Online Classification Learner

Gary R. Marrs, Ray J. Hickey, Michaela Black

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

An online classification system maintained by a learner can besubject to latency and filtering of training examples which can impact on itsclassification accuracy especially under concept drift. A life-cycle model isdeveloped to provide a framework for studying this problem. Meta dataemerges from this model which it is proposed can enhance online learningsystems. In particular, the definition of the time-stamp of an example, ascurrently used in the literature, is shown to be problematic and an alternative isproposed.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages8
Publication statusPublished - 24 Sep 2010
EventHaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010) Barcelona
Duration: 24 Sep 2010 → …

Workshop

WorkshopHaCDAIS 2010: International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions
Period24/09/10 → …

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  • Cite this

    Marrs, G. R., Hickey, R. J., & Black, M. (2010). Modeling the Example Life-Cycle in an Online Classification Learner. In Unknown Host Publication IEEE.