Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Abstract

Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal ‘start’ or ‘end’; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.

DOI: 10.1007/s10710-014-9236-y

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Cite this paper

@article{Heywood2014EvolutionaryMB, title={Evolutionary model building under streaming data for classification tasks: opportunities and challenges}, author={Malcolm I. Heywood}, journal={Genetic Programming and Evolvable Machines}, year={2014}, volume={16}, pages={283-326} }