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; Mingjiang Li; Ikpenmosa Uhumuavbi; Celestine Iwendi

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

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
, Mingjiang Li
, Ikpenmosa Uhumuavbi
, Celestine Iwendi

Qiannan Normal College for Nationalities
Nanchang Institute of Science and Technology
University of Bolton

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	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
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	321-331
Number of pages	11
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_23
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

Keywords

Classification
Decision tree
K-NN
Language processing
Machine learning
Naive bayes
Random forest
SVM
Semi-supervised learning
Support vector machine
Text classification
Text processing

Access to Document

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

Cite this

Hai, T., Zhou, J., Zadeh, S. A., Adetiloye, O. A., Li, M., Uhumuavbi, I., & Iwendi, C. (2023). Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), 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; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_23

Hai, Tao ; Zhou, Jincheng ; Zadeh, Shirin Abolfath et al. / Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN. 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. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 321-331 (Lecture Notes in Networks and Systems).

@inproceedings{a5256ffcc5084a7c856c5dfc7e49f60c,

title = "Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN",

abstract = "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.",

keywords = "Classification, Decision tree, K-NN, Language processing, Machine learning, Naive bayes, Random forest, SVM, Semi-supervised learning, Support vector machine, Text classification, Text processing",

author = "Tao Hai and Jincheng Zhou and Zadeh, \{Shirin Abolfath\} and Adetiloye, \{Oluwabukola A.\} and Mingjiang Li and Ikpenmosa Uhumuavbi and Celestine Iwendi",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_23",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "321--331",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "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",

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Hai, T, Zhou, J, Zadeh, SA, Adetiloye, OA, Li, M, Uhumuavbi, I & Iwendi, C 2023, Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN. in C Iwendi, Z Boulouard & N Kryvinska (eds), 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. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 321-331, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_23

Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN. / Hai, Tao; Zhou, Jincheng; Zadeh, Shirin Abolfath et al.
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. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 321-331 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN

AU - Hai, Tao

AU - Zhou, Jincheng

AU - Zadeh, Shirin Abolfath

AU - Adetiloye, Oluwabukola A.

AU - Li, Mingjiang

AU - Uhumuavbi, Ikpenmosa

AU - Iwendi, Celestine

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Classification

KW - Decision tree

KW - K-NN

KW - Language processing

KW - Machine learning

KW - Naive bayes

KW - Random forest

KW - SVM

KW - Semi-supervised learning

KW - Support vector machine

KW - Text classification

KW - Text processing

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DO - 10.1007/978-3-031-37164-6_23

M3 - Conference contribution

AN - SCOPUS:85174517377

SN - 9783031371639

T3 - Lecture Notes in Networks and Systems

SP - 321

EP - 331

BT - 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

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

Y2 - 24 February 2023 through 25 February 2023

ER -

Hai T, Zhou J, Zadeh SA, Adetiloye OA, Li M, Uhumuavbi I et al. Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN. In Iwendi C, Boulouard Z, Kryvinska N, editors, 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. Springer Science and Business Media Deutschland GmbH. 2023. p. 321-331. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_23

Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN

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