title: Variable selection in multivariate calibration based on clustering of variable concept.
abstract: Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.
authors.affiliation: The Persian Gulf Marine Biotechnology Research Center, Bushehr University of Medical Sciences, Bushehr, Iran.
authors.forename: Maryam
authors.initials: M
authors.lastname: Farrokhnia
authors.affiliation: Department of Chemistry, College of Sciences, Persian Gulf University, Bushehr, Iran. Electronic address: karimi.sadegh@gmail.com.
authors.forename: Sadegh
authors.initials: S
authors.lastname: Karimi
mesh_terms: D002138:Calibration; D016000:Cluster Analysis; D016018:Least-Squares Analysis
keywords: Clustering of variable – partial least square; Interval based partial least square; Partial least square; Self organization map; Variable selection