Integer-based genetic algorithm for feature selection in multivariate calibration

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{sousa:2017:CEC,

• author = "Rhelcris S. Sousa and Telma W. {de Lima} and Lauro C. M. {de Paula} and Roney L. Lima and Arlindo R. G. Filho and Anderson S. Soares",
• booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
• title = "Integer-based genetic algorithm for feature selection in multivariate calibration",
• year = "2017",
• editor = "Jose A. Lozano",
• pages = "2315--2320",
• address = "Donostia, San Sebastian, Spain",
• publisher = "IEEE",
• month = "5-8 " # jun,
• keywords = "genetic algorithms, genetic programming, grammatical evolution, feature selection, integer programming, 2014 IDRC, binary encoding, dimensionality reduction, evolutionary algorithms, gas mixtures, integer-based GA implementation, integer-based genetic algorithm, model prediction error, multivariate calibration models, petroleum reservoirs, Calibration, Encoding, Mathematical model, Predictive models, Sociology, Statistics",
• isbn13 = "978-1-5090-4601-0",
• DOI = "doi:10.1109/CEC.2017.7969585",
• abstract = "Feature selection is a important task to reduce dimensionality in large datasets. Datasets from multivariate calibration problems are a good example of datasets with a large number of features. In literature, there are several types of techniques to reduce the number of features for this problem, among them, evolutionary algorithms such as genetic algorithms (GAs). They have been successfully used with binary encoding to select features in multivariate calibration. However, as far as we know, there is no work in literature which provides an integer encoding GA in such context. Thus, this paper presents an integer-based GA implementation for feature selection in multivariate calibration models. The results demonstrated that our proposal is able to outperform the outcomes of participants from 2014 IDRC regarding model prediction error as well as number of selected features. In this dataset, the samples correspond to oils from petroleum reservoirs around the world and gas mixtures in the gas phase measured in transmittance. The gain of our proposed implementation in relation to the winner was from 20.9percent up to 88.8percent.",
• notes = "Is this GP?

  IEEE Catalog Number: CFP17ICE-ART Also known as \cite{7969585}",

}

Genetic Programming entries for Rhelcris S Sousa Telma W de Lima Lauro C M de Paula Roney L Lima Arlindo R G Filho Anderson S Soares

Citations