Tracking concept drifting with an online-optimized incremental learning framework

Concept drifting is an important and challenging research issue in the field of machine learning. This paper mainly addresses the issue of semantic concept drifting in time series such as video streams over a relatively long period of time. An Online-Optimized Incremental Learning framework is proposed as an example learning system for tracking the drifting concepts. Furthermore, a set of measures are defined to track the process of concept drifting in the learning system. These tracking measures are also applied to determine the corresponding parameters used for model updating in order to obtain the optimal up-to-date classifiers. Experiments on the data set of TREC Video Retrieval Evaluation 2004 not only demonstrate the inside concept drifting process of the learning system, but also prove that the proposed learning framework is promising for tackling the issue of concept drifting.

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