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

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@Article{Heywood:2015:GPEM,

• author = "Malcolm I. Heywood",
• title = "Evolutionary model building under streaming data for classification tasks: opportunities and challenges",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2015",
• volume = "16",
• number = "3",
• pages = "283--326",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Streaming data, Non-stationary processes, Dynamic environment, Imbalanced data, Task decomposition, Ensemble learning, Active learning, Evolvability, Diversity, Memory",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-014-9236-y",
• size = "44 pages",
• 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.",

}

Genetic Programming entries for Malcolm Heywood

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