Random correlation in variable selection for multivariate calibration with a genetic algorithm

被引：69
|
作者：
JouanRimbaud, D
Massart, DL
deNoord, OE
机构：
[1] FREE UNIV BRUSSELS,INST PHARMACEUT,CHEMOAC,B-1090 BRUSSELS,BELGIUM
[2] SHELL INT RES MAATSCHAPPIJ BV,KONINKLIJKE SHELL LAB,NL-1030 BN AMSTERDAM,NETHERLANDS
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1996年 / 35卷 / 02期
关键词：
near-infrared spectroscopy; wavelength selection; genetic algorithm;
D O I：
10.1016/S0169-7439(96)00062-7
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The importance of the validation step in multiple linear regression of near-infrared spectroscopic data, after selection of wavelengths by a genetic algorithm, is investigated with the use of random variables. It is shown that in spite of a careful validation procedure, the GA can still select irrelevant variables. The effect is greatly reduced by applying a forward selection in the subsets selected by the genetic algorithm.
引用
收藏
页码：213 / 220
页数：8
相关论文
共 50 条
  • [1] Multiobjective Firefly Algorithm for Variable Selection in Multivariate Calibration
    Martins de Paula, Lauro Cassio
    Soares, Anderson da Silva
    [J]. PROGRESS IN ARTIFICIAL INTELLIGENCE-BK, 2015, 9273 : 274 - 279
  • [2] Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration
    Cheng Jie-hong
    Chen Zheng-guang
    Yi Shu-juan
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42 (03) : 719 - 725
  • [3] Integer-Based Genetic Algorithm for Feature Selection in Multivariate Calibration
    Sousa, Rhelcris S.
    de Lima, Telma W.
    de Paula, Lauro C. M.
    Lima, Roney L.
    Filho, Arlindo R. G.
    Soares, Anderson S.
    [J]. 2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2017, : 2315 - 2320
  • [4] An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra
    Sun, Jingjing
    Yang, Wude
    Feng, Meichen
    Liu, Qifang
    Kubar, Muhammad Saleem
    [J]. RSC ADVANCES, 2020, 10 (28) : 16245 - 16253
  • [5] Variable selection for neural networks in multivariate calibration
    Despagne, F
    Massart, DL
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1998, 40 (02) : 145 - 163
  • [6] Variable Selection and Reduction in Multivariate Calibration and Modelling
    Vander Heyden, Yvan
    Andries, Jan P. M.
    Goodarzi, Mohammad
    [J]. LC GC EUROPE, 2011, 24 (12) : 642 - 644
  • [7] Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study
    Attia, Khalid A. M.
    Nassar, Mohammed W. I.
    El-Zeiny, Mohamed B.
    Serag, Ahmed
    [J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2017, 170 : 117 - 123
  • [8] A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration
    Yun, Yong-Huan
    Cao, Dong-Sheng
    Tan, Min-Li
    Yan, Jun
    Ren, Da-Bing
    Xu, Qing-Song
    Yu, Ling
    Liang, Yi-Zeng
    [J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2014, 130 : 76 - 83
  • [9] Variable selection in multivariate calibration based on clustering of variable concept
    Farrokhnia, Maryam
    Karimi, Sadegh
    [J]. ANALYTICA CHIMICA ACTA, 2016, 902 : 70 - 81
  • [10] Variable interaction network based variable selection for multivariate calibration
    Rao, Raghuraj
    Lakshminarayanan, S.
    [J]. ANALYTICA CHIMICA ACTA, 2007, 599 (01) : 24 - 35
12345