Browsing by Author "Omokanye, Samuel Oladeji"
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Item Binary Text Classification Using An Ensemble of Naïve Bayes and Support Vector Machines(Georgian Technical University and St. Andrew the First Called Georgian University of The Patriarchy of Georgia, 2017) Abikoye, Oluwakemi Christiana; Omokanye, Samuel Oladeji; Aro, Taye OladeleText classification is being done by classifiers over the years, combining classifiers together can result in better classification and thus Naïve Bayes algorithm is combined with Support vector machine through stacking and the results shows that the ensemble results in an increase in the classification accuracy though at the expense of the time taken by the ensemble to build its classification modelItem Text Classification Using Data Mining Techniques: A Review(School of Computing, Engineering and Physical Science, University of the West of Scotland, Paisley., 2018-05) Abikoye, Oluwakemi Christiana; Omokanye, Samuel Oladeji; Aro, Taye Oladelemining algorithms used for text classification and a review of works that have been performed on classifying texts. Design/Methodology/Approach: Data mining algorithms used for text classification were discussed and researches done on applying such algorithms in classifying texts were considered with more emphasis on comparative studies. Findings: No classifier can perform best in all situations as different datasets and conditions bring about different classification accuracies. Practical Implications: In applying data mining algorithms for classifying text documents, it should be noted that the conditions of the data will affect classification accuracy; therefore such data should be well presented. Researchers may also need to try different algorithms and conditions to get a desired level of accuracy. Originality/Value: A lot of work has been done in reviewing of data mining algorithms but this research has its specific emphasis on text and in addition to previous reviews, more recent journals were considered