Fast Dual Selection using Genetic Algorithms for Large Data Sets

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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Provided by:
Machine Intelligence Research Labs (MIR Labs)
Big Data