Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Pasadakis Nikos, Andreas Kardamakis

Simple record


URIhttp://purl.tuc.gr/dl/dias/65257598-A57F-4B13-BA19-A008C433388D-
Languageen-
TitleClassification of gasoline samples using variable reduction and expectation-maximization methodsen
CreatorPasadakis Nikosen
CreatorΠασαδακης Νικοςel
Creator Andreas Kardamakisen
PublisherBrill Academic Publishersen
Content SummaryGasoline 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
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-01-
Date of Publication2006-
Bibliographic CitationNikos 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

Available Files

Services

Statistics