The impact of transformed features in automating the Swahili document classification

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dc.contributor.authorTesha, Thomas
dc.date.accessioned2023-10-12T12:22:19Z
dc.date.available2023-10-12T12:22:19Z
dc.date.issued2015
dc.descriptionAbstract. Full text article. Also available at https://tinyurl.com/4tjhm7mxen_US
dc.description.abstractThis paper describes experimental results in an attempt to identify the Transformation techniques which can be adopted to improve features for the automation of classification of Swahili documents. This means improving classification rate by enhancing separability and accuracy. The experiment involved Relative Frequency (RF), Power transformation (PT) and Relative Frequency with Power transformation (RFPT). The Term weighting with TFIDF and the absolute features (AF) were also studied. The features’ dimension reduction was done by using the statistical techniques of Principal Component Analysis. In learning algorithm, the Support vector machine for classification and the k-NN were used, and in evaluating the effect of features’ performance with the classifiers the micro averaged f-measure were adopted. The extensive experimental results demonstrated that the RFPT features worked better with the Support Vector Machine classifiers unlike k-NN in improving the classification rate by enhancing document separability and accuracy in Automation of Swahili document classification.en_US
dc.identifier.citationTesha, T. (2015). The Impact of Transformed Features in Automating the Swahili Document Classification. International Journal of Computer Applications, 975, 8887.en_US
dc.identifier.otherURL: https://tinyurl.com/4tjhm7mx
dc.identifier.urihttp://hdl.handle.net/20.500.12661/4137
dc.language.isoenen_US
dc.publisherFoundation of Computer Scienceen_US
dc.subjectMachine learning algorithmen_US
dc.subjectSupport vector machineen_US
dc.subjectSwahilien_US
dc.subjectSwahili document classificationen_US
dc.subjectDocument transformation techniquesen_US
dc.subjectSwahili documentsen_US
dc.subjectLearning algorithmen_US
dc.titleThe impact of transformed features in automating the Swahili document classificationen_US
dc.typeArticleen_US
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