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Fast dual selection using genetic algorithms for large data sets
2012
2012 12th International Conference on Intelligent Systems Design and Applications (ISDA)
This paper is devoted to feature and instance selection managed by genetic algorithms (GA) in the context of supervised classification. We 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 our approach is the variable fitness evaluation based on
doi:10.1109/isda.2012.6416642
dblp:conf/isda/RosHP12
fatcat:dhojalvrpjembfg35gziuyayvm