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Classification of gasoline samples using variable reduction and expectation-maximization methods

Pasadakis Nikos, Andreas Kardamakis

Απλή Εγγραφή

URIhttp://purl.tuc.gr/dl/dias/65257598-A57F-4B13-BA19-A008C433388D-
Γλώσσαen-
ΤίτλοςClassification of gasoline samples using variable reduction and expectation-maximization methodsen
ΔημιουργόςPasadakis Nikosen
ΔημιουργόςΠασαδακης Νικοςel
Δημιουργός Andreas Kardamakisen
ΕκδότηςBrill Academic Publishersen
ΠερίληψηGasoline classification is an important issue in environmental and forensic applications. Several categorization algorithms exist that attempt to correctly classify gasoline samples in data sets. We demonstrate a method that can improve classification performance by maximizing hit-rate without using a priori knowledge of compounds in gasoline samples. This is accomplished by using a variable reduction technique that de-clutters the data set from redundant information by minimizing multivariate structural distortion and by applying a greedy Expectation-Maximization (EM) algorithm that optimally tunes parameters of a Gaussian mixture model (GMM). These methods initially classify premium and regular gasoline samples into clusters relying on their gas chromatography-mass spectroscopy (GC-MS) spectral data and then they discriminate them into their winter and summer subgroups. Approximately 89% of the samples were correctly classified as premium or regular gasoline and 98.8% of the samples were correctly classified according to their seasonal characteristics. en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2015-11-01-
Ημερομηνία Δημοσίευσης2006-
Βιβλιογραφική ΑναφοράNikos Pasadakis and Andreas Kardamakis, “Classification of gasoline samples using variable reduction and expectation-maximization methods”, in International Conference of Computational Methods in Sciences and Engineering, Brill Academic Publishers, 2006, pp.435-437.en

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