Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN

Tao Hai; Jincheng Zhou; Shirin Abolfath Zadeh; Oluwabukola A. Adetiloye; Mingguang Li; Ikpenmosa Uhumuavbi; Celestine Iwendi

doi:10.1007/978-3-031-37164-6_23

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Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN

Conference proceeding

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Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN

Tao Hai, Jincheng Zhou, Shirin Abolfath Zadeh, Oluwabukola A. Adetiloye, Mingguang Li, Ikpenmosa Uhumuavbi and Celestine Iwendi

Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering New Artificial Intelligence and the Internet of Things Based Perspective and Solutions, pp.321-331

Lecture Notes in Networks and Systems, 735

ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)

24/09/2023

DOI: https://doi.org/10.1007/978-3-031-37164-6_23

Abstract

text processing

text classification

Support Vector Machine

SVM

language processing

classification

semi-supervised learning

Naive Bayes

Random Forest

Decision Tree

K-NN

Machine Learning

This paper aims to find the boost model which brings the best accuracy in text classification by using Support Vector Machine in comparison with other models namely Naive Bayes, Random Forest Decision Tree and K-NN. For the text classification and processing, the planned system will have to apply with the Support Vector Machine and the result is decided by major roles. Based on the Machine Learning algorithms used for the implementation of the research- the BBC news dataset- illustrates that the Support Vector Machine has better accuracy and result.

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Title: Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN
Creators - without role: Tao Hai
Jincheng Zhou - Qiannan Normal College For Nationalities
Shirin Abolfath Zadeh - University of Greater Manchester
Oluwabukola A. Adetiloye - University of Greater Manchester
Mingguang Li
Ikpenmosa Uhumuavbi - University of Greater Manchester
Celestine Iwendi - University of Greater Manchester
Contributors: Celestine Iwendi (Editor) - University of Greater Manchester
Z. Boulouard (Editor)
N. Kryvinska (Editor)
Publication Details: Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering New Artificial Intelligence and the Internet of Things Based Perspective and Solutions, pp.321-331
Conference: ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)
Series: Lecture Notes in Networks and Systems; 735
Publisher: Springer; Cham
Identifiers: 999907108841; 2367-3370; 2367-3389; 9783031371639; 2367-3389
Academic Unit: Computing; Games; Law
Language: English
Resource Type: Conference proceeding
Comment: Paper given at and published in the Proceedings of ICACTCE23, 24-25th February 2023, University of Bolton
Generative AI Statement: https://icactce-conf.com/

Evaluation of text classification using Support Vector Machine compare with Naive Bayes, Random Forest Decision Tree and K-NN

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