Feature selection using geometric semantic genetic programming
Authors: 
G. H. Rosa, J. P. Papa, L. P. PapaAuthors Info & Claims
G. H. Rosa, J. P. Papa, L. P. PapaAuthors Info & ClaimsPages 253 - 254
Abstract
Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been modeled as an optimization task, where the idea is to find the subset of features that maximize some fitness function, which can be a given classifier's accuracy or even some measure concerning the samples' separability in the feature space, for instance. In this paper, we introduced Geometric Semantic Genetic Programming (GSGP) in the context of feature selection, and we experimentally showed it can work properly with both conic and non-conic fitness landscapes.
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- Feature selection using geometric semantic genetic programming
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Published In
July 2017
1934 pages
ISBN:9781450349390
DOI:10.1145/3067695
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Published: 15 July 2017
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