Cited By
View all- Paula L(2021)Epistasis-Based Feature Selection AlgorithmEpistasis10.1007/978-1-0716-0947-7_3(37-44)Online publication date: 18-Mar-2021
Variable selection as a non-completely decomposable problem: a case study in multivariate calibration
Authors: 
Lauro C. M. de Paula, 
Anderson S. Soares, Telma W. Soares, Clarimar J. CoelhoAuthors Info & Claims
Pages 1399 - 1402
Abstract
Variable selection is a procedure used to choose a subset of features in order to extract information from them. It has been widely used in multivariate calibration together with statistical techniques to build a model from which it is possible to be interpreted by users. Genetic algorithms (GAs) have been successfully utilized as a variable selection method in multivariate calibration models. However, GAs solve a problem by trying different decompositions, and the variable selection problem usually can not be properly decomposed when there is considerable correlation among variables. Consequently GAs tend to lead to a poor variable selection performance if the variables interdepence is strong. This work comes from a doctoral thesis, which is still in development and aims to (not only) demonstrate that selecting variables in multivariate calibration is a non-completely decomposable problem. Based on the preliminary results, we are able to claim the viability of our initial hypothesis.
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Lauro Cassio Martins Paula, Anderson Silva Soares, Telma W. Lima, Clarimar Jose Coelho, and Arlindo R. G. Filho. 2016. Variable Selection for Multivariate Calibration in Chemometrics: A Real-World Application with Building Blocks Disruption Problem. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (GECCO). ACM, 1031--1034.
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- Variable selection as a non-completely decomposable problem: a case study in multivariate calibration
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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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View all- Paula L(2021)Epistasis-Based Feature Selection AlgorithmEpistasis10.1007/978-1-0716-0947-7_3(37-44)Online publication date: 18-Mar-2021
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