Analysis of Naive Bayesian and Back Propagation algorithms in iris classification

Research Article
Open access

Analysis of Naive Bayesian and Back Propagation algorithms in iris classification

Chengyang Yu 1*
  • 1 Dalian University of Technology    
  • *corresponding author 20211072012@mail.dlut.edu.cn
Published on 7 February 2024 | https://doi.org/10.54254/2755-2721/37/20230467
ACE Vol.37
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-299-2
ISBN (Online): 978-1-83558-300-5

Abstract

An efficient taxonomy of irises can provide botanists with valuable tools. Machine learning algorithms can effectively improve the performance of iris classification models because they can automatically analyze and summarize data. To this end, this paper introduces Naive Bayesian (NB) and Back Propagation (BP) to build classification models. When creating the NB model, the petal and sepal data from the iris dataset are used sequentially as classification criteria to classify the data. When constructing the BP model, the author sets different iterations and outputs the loss function and accuracy of the BP model under different iterations. The study finds that the NB model has higher classification accuracy when using petal length and petal width as classification criteria, which is 17% higher than the classification accuracy using sepal length and sepal width. Therefore, the NB model is more suitable for classifying independent data. By studying the use of the BP algorithm to classify iris flowers, the automatic classification of iris flowers can be realized and the accuracy of classification can be improved. Compared with the traditional NB algorithm, the BP algorithm can better mine the hidden patterns and information in the iris data and make effective classifications. This study provides new insights and discoveries for the taxonomic study of Iris plants.

Keywords:

iris classification, naive bayesian, back propagation

Yu,C. (2024). Analysis of Naive Bayesian and Back Propagation algorithms in iris classification. Applied and Computational Engineering,37,38-44.
Export citation

References

[1]. Rumelhart D E Hinton G E Williams R J 1986 Representations by Back propagatingerrors Nature 323(6088): pp 533-536

[2]. Mukherjee A Jain D K 2020 Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation System IEEE pp 28524-28532

[3]. Webb G I 2017 Naïve Bayes Encyclopedia of Machine Learning and Data Mining pp 1-2

[4]. Kang C L 2020 Application of Neural Networks Learned by Mentors in Iris Species Recognition Xinzhou Demonstration School Report pp 17-21

[5]. Pandey A Jain A 2017 Comparative Analysis of KNN Algorithm using Various Normalization Techniques I.J.Computer Network and Information Security pp 36-42

[6]. Wu Y Y He J Ji Y M Huang G L Yao H C Zhang P Xu W Guo M J Li Y T 2019 Enhanced Classification Models for Iris Dataset Procedia Computer Science pp 946-954

[7]. Rana D Jena S P Pradhan S K 2020 Performance Comparison of PCA and LDA with Linear Regression and Random Forest for IRIS Flower Classification PalArch’s Journal of Archaeology of Egypt/Egyptology pp 2825-2830

[8]. Hussain Z F Ibraheem H R 2020 A new model for iris data set classification based on linear support vector machine parameter’s optimization pp 1079-1084

[9]. Marques-Silva J Gerspacher T 2020 Explaining Naïve Bayes and Other Linear Classifiers with Polynomial Time and Delay Advance in Neural Information Processing System p 33

[10]. Sang B 2020 Application of genetic algorithm and BP neural network in supply chain finance under information sharing Journal of Computational and Applied Mathematics 384

[11]. Iris dataset https://archive.ics.uci.edu/dataset/53/iris

Cite this article

Yu,C. (2024). Analysis of Naive Bayesian and Back Propagation algorithms in iris classification. Applied and Computational Engineering,37,38-44.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-299-2(Print) / 978-1-83558-300-5(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.37
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Rumelhart D E Hinton G E Williams R J 1986 Representations by Back propagatingerrors Nature 323(6088): pp 533-536

[2]. Mukherjee A Jain D K 2020 Back Propagation Neural Network Based Cluster Head Identification in MIMO Sensor Networks for Intelligent Transportation System IEEE pp 28524-28532

[3]. Webb G I 2017 Naïve Bayes Encyclopedia of Machine Learning and Data Mining pp 1-2

[4]. Kang C L 2020 Application of Neural Networks Learned by Mentors in Iris Species Recognition Xinzhou Demonstration School Report pp 17-21

[5]. Pandey A Jain A 2017 Comparative Analysis of KNN Algorithm using Various Normalization Techniques I.J.Computer Network and Information Security pp 36-42

[6]. Wu Y Y He J Ji Y M Huang G L Yao H C Zhang P Xu W Guo M J Li Y T 2019 Enhanced Classification Models for Iris Dataset Procedia Computer Science pp 946-954

[7]. Rana D Jena S P Pradhan S K 2020 Performance Comparison of PCA and LDA with Linear Regression and Random Forest for IRIS Flower Classification PalArch’s Journal of Archaeology of Egypt/Egyptology pp 2825-2830

[8]. Hussain Z F Ibraheem H R 2020 A new model for iris data set classification based on linear support vector machine parameter’s optimization pp 1079-1084

[9]. Marques-Silva J Gerspacher T 2020 Explaining Naïve Bayes and Other Linear Classifiers with Polynomial Time and Delay Advance in Neural Information Processing System p 33

[10]. Sang B 2020 Application of genetic algorithm and BP neural network in supply chain finance under information sharing Journal of Computational and Applied Mathematics 384

[11]. Iris dataset https://archive.ics.uci.edu/dataset/53/iris

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.