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

Authors: 

Malcolm I. Heywood

Author Addresses: 

Faculty of Computer Science
Dalhousie University
6050 University Ave.
PO Box 15000
Halifax, Nova Scotia, Canada
B3H 4R2

Abstract: 

Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches tothe 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 func- tion 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 both the perspective of 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.

Tech Report Number: 
CS-2016-01
Report Date: 
April 21, 2016
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