کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1180850 962875 2006 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
موضوعات مرتبط
مهندسی و علوم پایه شیمی شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products
چکیده انگلیسی

In this paper we apply the recently introduced Random Forest-Recursive Feature Elimination (RF-RFE) algorithm to the identification of relevant features in the spectra produced by Proton Transfer Reaction-Mass Spectrometry (PTR-MS) analysis of agroindustrial products. The method is compared with the more traditional Support Vector Machine-Recursive Feature Elimination (SVM-RFE), extended to allow multiclass problems, and with a baseline method based on the Kruskal–Wallis statistic (KWS). In particular, we apply all selection methods to the discrimination of nine varieties of strawberries and six varieties of typical cheeses from Trentino Province, North Italy. Using replicated experiments we estimate unbiased generalization errors. Our results show that RF-RFE outperforms SVM-RFE and KWS on the task of finding small subsets of features with high discrimination levels on PTR-MS data sets. We also show how selection probabilities and features co-occurrence can be used to highlight the most relevant features for discrimination.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 83, Issue 2, 15 September 2006, Pages 83–90
نویسندگان
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