کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7121430 | 1461467 | 2018 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Geographical recognition of Syrah wines by combining feature selection with Extreme Learning Machine
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Data mining techniques have been used for the classification of many types of products. In order to classify the Syrah wines from Argentina (Mendoza) and Chile (Central Valley), according to their origin, we perform two feature selection methods with the following classification algorithms: Support Vector Machines (SVM), and two types of artificial neural networks, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), on 10-fold cross-validation. Each feature selection method has a different approach, creating also different sets of the most important features. The best model was the combination of variables peon-3-glu, malv-3-glu and pet-3-acetylglu, selected by Random Forest Importance, reaching 98.33% accuracy with ELM, outperforming SVM and MLP. The results obtained from the classifiers and feature subsets are able to confirm the importance of the anthocyanins to classify Syrah wines according to their geographic region. ELM was the best algorithm for classifying Syrah wines.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Measurement - Volume 120, May 2018, Pages 92-99
Journal: Measurement - Volume 120, May 2018, Pages 92-99
نویسندگان
Nattane LuÃza da Costa, Laura Andrea GarcÃa Llobodanin, Márcio Dias de Lima, Inar Alves Castro, Rommel Barbosa,