Performance of Classifiers on Newsgroups using Specific Subset of Terms
Deepanshu*, M.Tech Department of Computer Science and Applications,
Kurukshetra University, Kurukshetra, Haryana, India.
Manuscript received on October 17, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2497-2500 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4652119119/2019©BEIESP | DOI: 10.35940/ijitee.A4652.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Text Classification plays a vital role in the world of data mining and same is true for the classification algorithms in text categorization. There are many techniques for text classification but this paper mainly focuses on these approaches Support vector machine (SVM), Naïve Bayes (NB), k-nearest neighbor (k-NN). This paper reveals results of the classifiers on mini-newsgroups data which consists of the classifies on mini-newsgroups data which consists a lot of documents and step by step tasks like a listing of files, preprocessing, the creation of terms(a specific subset of terms), using classifiers on specific subset of datasets. Finally, after the results and experiments over the dataset, it is concluded that SVM achieves good classification output corresponding to accuracy, precision, F-measure and recall but execution time is good for the k-NN approach.
Keywords: Classifiers, k-NN, NaïveBayes, Text Classification, SVM.
Scope of the Article: Classification