Modeling the Example Life-Cycle in an Online Classification Learner

Abstract. An online classification system maintained by a learner can be subject to latency and filtering of training examples which can impact on its classification accuracy especially under concept drift. A life-cycle model is developed to provide a framework for studying this problem. Meta data emerges from this model which it is proposed can enhance online learning systems. In particular, the definition of the time-stamp of an example, as currently used in the literature, is shown to be problematic and an alternative is proposed