Fast Dual Selection using Genetic Algorithms for Large Data Sets

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Provided by: Machine Intelligence Research Labs (MIR Labs)
Topic: Big Data
Format: PDF
In this paper the authors devoted to feature and instance selection managed by Genetic Algorithms (GA) in the context of supervised classification. They propose a GA encoded by binary chromosomes having the same size as the feature space for selecting features in which each evaluated chromosome delivers a set of instances. The main aim is to optimize the processing time, which is particularly problematic when handling large databases. A key feature of their approach is the variable fitness evaluation based on scalability methodologies.
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