title: A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.
abstract: Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
authors.forename: Yong-Huan
authors.initials: YH
authors.lastname: Yun
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
authors.forename: Wei-Ting
authors.initials: WT
authors.lastname: Wang
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
authors.forename: Min-Li
authors.initials: ML
authors.lastname: Tan
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China. Electronic address: yizeng_liang@263.net.
authors.forename: Yi-Zeng
authors.initials: YZ
authors.lastname: Liang
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
authors.forename: Hong-Dong
authors.initials: HD
authors.lastname: Li
authors.affiliation: College of Pharmaceutical Sciences, Central South University, Changsha 410083, PR China.
authors.forename: Dong-Sheng
authors.initials: DS
authors.lastname: Cao
authors.affiliation: College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
authors.forename: Hong-Mei
authors.initials: HM
authors.lastname: Lu
authors.affiliation: School of Mathematics and Statistics, Central South University, Changsha 410083, PR China.
authors.forename: Qing-Song
authors.initials: QS
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D002138:Calibration; D020407:Internet; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012984:Software; D013024:Soybean Oil / Q000737:chemistry; D019265:Spectroscopy, Near-Infrared / Q000592:standards; D014867:Water / Q000032:analysis / Q000592:standards; D003313:Zea mays / Q000737:chemistry
chemical_list: D014867:Water; D013024:Soybean Oil
keywords: Informative variables; Iteratively retaining informative variables; Partial least squares; Random combination; Variable selection