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论文类型：期刊论文

发表时间：2010-09-15

发表刊物：TALANTA

收录刊物：SCIE、EI、PubMed、Scopus

卷号：82

期号：4

页面范围：1571-1575

ISSN号：0039-9140

关键字：Random forest; Combined features; Cut tobacco; GC-TOF MS

摘要：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 Delta t. 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 Delta t 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 Naive Bayes, support vector machines, bootstrap aggregating and the random forest using only its original features. (C) 2010 Elsevier B.V. All rights reserved.