title: Application of random forests method to predict the retention indices of some polycyclic aromatic hydrocarbons.
abstract: In this work, a quantitative structure-retention relationship (QSRR) investigation was carried out based on the new method of random forests (RF) for prediction of the retention indices (RIs) of some polycyclic aromatic hydrocarbon (PAH) compounds. The RIs of these compounds were calculated using the theoretical descriptors generated from their molecular structures. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. Optimization of these parameters showed that in the point m=70, nt=460, the RF method can give the best results. Also, performance of the RF model was compared with that of the artificial neural network (ANN) and multiple linear regression (MLR) techniques. The results obtained show the relative superiority of the RF method over the MLR and ANN ones.
authors: Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran. Electronic address: goudarzi@shahroodut.ac.ir.
authors: N
authors: N
authors: Goudarzi
authors: Faculty of Mathematics, Shahrood University of Technology, Shahrood, Iran.
authors: D
authors: D
authors: Shahsavani
authors: Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran.
authors: F
authors: F
authors: Emadi-Gandaghi
authors: Faculty of Chemistry, Shahrood University of Technology, Shahrood, Iran.
authors: M Arab
authors: MA
authors: Chamjangali
mesh_terms: D000465:Algorithms; D008401:Gas Chromatography-Mass Spectrometry; D016014:Linear Models; D016571:Neural Networks, Computer; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry*
chemical_list: D011084:Polycyclic Aromatic Hydrocarbons
keywords: Artificial neural network (ANN); Polycyclic aromatic hydrocarbons (PAHs); Quantitative structure–retention relationship (QSRR); Random forest (RF)