Vehicle image classification using data mining techniques

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creativework.keywordsClassifiers; Data Mining; Image Preprocessing; Vehicle Classification; Weka
dc.contributor.authorSuguitan, Agnes S. .
dc.contributor.authorDacaymat, Lucille N.
dc.date.accessioned2026-09-10T06:14:18Z
dc.date.available2026-09-10T06:14:18Z
dc.date.issued2019-05-24
dc.descriptionFull text
dc.description.abstractThis paper focuses on the application of different data mining techniques to classify images of vehicles into three classes using Weka. The dataset used for this study were collected from Google Image search engine and other dataset websites. In preprocessing the images, filters such as Color Layout, Edge Histogram and Pyramid Histogram of Oriented Gradients were explored to extract the image features from the dataset. Classification techniques such as Multilayer Perceptron, Sequential Minimal Optimization, Logistic Model Trees, Simple Logistic and Random Forest were used. Results of the study showed that the edge histogram features provided much information to the classifiers in order to correctly classify the images. The SMO classifier performs best with the highest accuracy of 82.37%.
dc.identifier.citationSuguitan, A. S. & Dacaymat, L. N. (2019). Vehicle image classification using data mining techniques. ACM International Conference Proceeding Series, 13 - 17. https://10.1145/3339363.3339366
dc.identifier.doihttps://10.1145/3339363.3339366
dc.identifier.isbn978-145037172-8
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2478
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.sdgSDG 9
dc.subjectMachine learning
dc.subjectPattern recognition
dc.subject.ddcData mining
dc.subject.lcshComputer vision
dc.subject.lcshImage processing--Digital techniques
dc.titleVehicle image classification using data mining techniques
dc.typeConference paper
oaire.citation.endPage17
oaire.citation.startPage13
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