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Large-scale data mining using genetics-based machine learning
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DOI：10.1002/widm.1078.png)
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Abstract
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In the last decade, genetics-based machine learning methods have shown their competence in large-scale data mining tasks because of the scalability capacity that these techniques have demonstrated. This capacity goes beyond the innate massive parallelism of evolutionary computation methods by the proposal of a variety of mechanisms specifically tailored for machine learning tasks, including knowledge representations that exploit regularities in the datasets, hardware accelerations or data-intensive computing methods, among others. This paper reviews different classes of methods that alone or (in many cases) combined accelerate genetics-based machine learning methods. (c) 2013 Wiley Periodicals, Inc.
Keywords:
CLASSIFIER-SYSTEMS
DISTRIBUTION ALGORITHMS
SUBSET-SELECTION
XCS
PREDICTION
ENSEMBLES
EVOLUTIONARY
PERFORMANCE
TAXONOMY
DOMAIN
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