title: A random forest of combined features in the classification of cut tobacco based on gas chromatography fingerprinting.
abstract: We applied the random forest method to discriminate among different kinds of cut tobacco. To overcome the influence of the descending resolution caused by column pollution and the subsequent deterioration of column efficacy at different testing times, we constructed combined peaks by summing the peaks over a specific elution time interval Deltat. On constructing tree classifiers, both the original peaks and the combined peaks were considered. A data set of 75 samples from three grades of the same tobacco brand was used to evaluate our method. Two parameters of the random forest were optimized using out-of-bag error, and the relationship between Deltat and classification rate was investigated. Experiments show that partial least squares discriminant analysis was not suitable because of the overfitting, and the random forest with the combined features performed more accurately than Naïve Bayes, support vector machines, bootstrap aggregating and the random forest using only its original features.
authors.affiliation: Department of Computer Science & Engineering, Dalian University of Technology, Dalian 116024, China.
authors.forename: Xiaohui
authors.initials: X
authors.lastname: Lin
authors.forename: Lie
authors.initials: L
authors.lastname: Sun
authors.forename: Yong
authors.initials: Y
authors.lastname: Li
authors.forename: Ziming
authors.initials: Z
authors.lastname: Guo
authors.forename: Yanli
authors.initials: Y
authors.lastname: Li
authors.forename: Kejun
authors.initials: K
authors.lastname: Zhong
authors.forename: Quancai
authors.initials: Q
authors.lastname: Wang
authors.forename: Xin
authors.initials: X
authors.lastname: Lu
authors.forename: Yuansheng
authors.initials: Y
authors.lastname: Yang
authors.forename: Guowang
authors.initials: G
authors.lastname: Xu
mesh_terms: D001499:Bayes Theorem; D002849:Chromatography, Gas / Q000379:methods*; D014026:Nicotiana