DATA MINING IN IDENTIFYING PREMIUM AND REGULAR GASOLINE USING SUPPORT VECTOR MACHINES AS NOVEL APPROACH FOR ARSON AND FUEL SPILL INVESTIGATION

Abstract

In this work, a novel data mining model based on Support Vector Machines (SVM) for the identification of gasoline types has been investigated and developed. Detection and correct identification of gasoline types during Arson and Fuel Spill Investigation are very important in forensic science. As the number of arson and spillage becomes a common place, it becomes more important to have an accurate means of detecting and classifyin gg asoline found at such sites of incidence. However, currently only a very few number of classification models have been explored in this germane field of forensic science, particularly as relates to gasoline identification. Thus, we have developed Support Vector Machines (SVM) based identification model for identifyin gg asoline types. The model was constructed usin gg as chromatography–mass spectrometry (GC–MS) spectral data obtained from gasoline sold in Canada over one calendar year. Prediction accuracy of the model was evaluated and compared with earlier used methods on the same datasets. Empirical results from simulation showed that SVM based model produced accurate and promising results better than the best among the other earlier implemented Artificial Neural Network and Principal Component Analysis methods on the same datasets.