Investigating Tree-Based Classifiers and Selected Ensemble Learning on Iris Flower Species Classification

International Journal of Computer Science and Engineering

© 2023 by SSRG - IJCSE Journal

Volume 10 Issue 5

Year of Publication : 2023

Authors : Ramoni Tirimisiyu Amosa, Adekiigbe Adebanjo, Fabiyi Aderanti Alifat, Olorunlomerue Adam Biodun, Oni Esther Kemi, Adejola Aanu Adeyinka, Adigun Olajide Israel, Joseph Babatunde Isaac

10.14445/23488387/IJCSE-V10I5P105

How to Cite?

Ramoni Tirimisiyu Amosa, Adekiigbe Adebanjo, Fabiyi Aderanti Alifat, Olorunlomerue Adam Biodun, Oni Esther Kemi, Adejola Aanu Adeyinka, Adigun Olajide Israel, Joseph Babatunde Isaac, "Investigating Tree-Based Classifiers and Selected Ensemble Learning on Iris Flower Species Classification," SSRG International Journal of Computer Science and Engineering , vol. 10,  no. 5, pp. 33-39, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I5P105

Abstract:

Eloquence, hope, knowledge, the ability to communicate effectively, and faith are some of the meanings associated with the iris flower in the language of flowers. Iris has different species types, and each type has its own medicinal purpose. Classifying the flower has become a serious task for researchers due to the high volume of datasets (big data), hence the introduction of machine learning algorithms for accurate and reliable classification. This paper focuses on the classification of the Iris flower using five tree-based algorithms; Best First Tree (BFTree), Least Absolute deviation Tree (LADTree), Cost-Sensitive Decision Forest (CSForest), Functional Tree (FT) and Random Tree (RT). Three selected ensemble learning (Bagging, Dagging and cascade generalisation) were equally implemented in the algorithm. The dataset that was utilised in this investigation is open source and may be downloaded without cost from a public repository (kaggle.com). The result of the classification showed that the FT classifiers outperform other tree-based classifiers with an accuracy of 96.67% and an AUC of 1.00. The ensemble algorithm has a significant impact on the performance of single classifiers (tree-based). Outperform tree based. AUC/ROC (Area Under Curve/Receiver Operating Characteristics) was used to evaluate the algorithm's performance. Bagging ensemble outperforms other ensembles (Dagging and Cascade) with an accuracy of 96.00% and AUC of 1.00.

Keywords:

Algorithm, Classification, Dataset, Ensemble, Performance.

References:

[1] Neda Ahmadi et al., “An Intelligent Method for Iris Recognition using Supervised Machine Learning Techniques,” Optics & Laser Technology, vol. 120, p. 105701, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] B. Angkita, and V. Deval, “Recognition of Iris Flower Species using Spatial Features,” NeuroQuantology, vol. 20, no. 14, pp. 880-886, 2022.
[3] Anju Gulia, Rajan Vohra, and Praveen Rani, “Liver Patient Classification using Intelligent Techniques,” International Journal of Computer Science and Information Technologies, vol. 5, no. 4, pp. 5110-5115, 2014.
[Google Scholar] [Publisher Link]
[4] T. Shashidhar Halakatti, and Shambulinga T. Halakatti, “Identification of Iris Flower Species using Machine Learning,” IPASJ International Journal of Computer Science (IIJCS), vol. 5, no. 8, pp. 59-69, 2017.
[Google Scholar]
[5] Zahra Faiz Hussain et al., “A New Model for Iris Data Set Classification Based on Linear Support Vector Machine Parameter's Optimization,” International Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 1079-1084, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Amar Yehya Hussien, “Flower Species Recognition Using Machine Learning Classifiers,” Academic Journal of Nawroz University, vol. 11, no. 4, pp. 469-475, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] G.A. Vijayalakshmi Pai, and S. Rajasekaran, Neural Networks, Fuzzy Logic and Genetic Algorithms Synthesis and Applications, 6th Edition, Prentice Hall of India Pvt. Ltd., 2006.
[8] Swati Rath, and Santanu Rath, “An Evolutionary Approach for Protein Classification Using Feature Extraction by Artificial Neural Network,” International Conference on Computer and Communication Technology (ICCCT), 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Asmita Shukla et al., “Flower Classification Using Supervised Learning,” International Journal of Engineering Research and Technology, vol. 9, no. 5, pp. 757-762, 2020.
[Google Scholar] [Publisher Link]
[10] Ahmed Hussein Ali, “A Survey on Vertical and Horizontal Scaling Platforms for Big Data Analytics,” International Journal of Integrated Engineering, vol. 11, no. 6, pp. 138-150, 2019.
[Google Scholar] [Publisher Link]
[11] Shahreen Kasim et al., “A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4-2, pp. 1581-1588, 2017.
[Google Scholar] [Publisher Link]
[12] Shahreen Kasim et al., “A Study on Facial Expression Recognition Using Local Binary Pattern,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1621-1626, 2017.
[Google Scholar] [Publisher Link]
[13] Nur Ariffin Mohd Zin et al., “Contact Lens Classification by Using Segmented lens Boundary Features,” Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), vol. 11, no. 3, pp. 1129-1135, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Debaraj Rana, Swarna Prabha Jena, and Subrat Kumar Pradhan, “Performance Comparison of PCA and LDA with Linear Regression and Random Forest for Iris Flower Classification,” PalArch's Journal of Archaeology of Egypt/Egyptology, vol. 17, no. 9, pp. 2353-2360, 2020.
[Google Scholar] [Publisher Link]
[15] Madhusmita Swain et al., “An Approach for Iris Plant Classification using Neural Network,” International Journal on Soft Computing, vol. 3, no. 1, pp. 79-89, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Lulin Ma et al., “Transcriptome Analysis of Key Genes Involved in Color Variation between Blue and White Flowers of Iris Bulleyana,” BioMed Research International, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Shivam Vatshayan, “Performance Evaluation of Supervised Learning for Iris Flower Species,” International Journal of Innovative Research in Technology, vol. 5, no. 11, 2019.
[Google Scholar] [Publisher Link]
[18] Joylin Priya Pinto, Soumya Kelur, and Jyothi Shetty, “Iris Flower Species Identification using Machine Learning Approach,” 2018 4th International Conference for Convergence in Technology (I2CT), 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Sajida Perveen et al., “A Systematic Machine Learning based Approach for the Diagnosis of Non-alcoholic Fatty Liver Disease Risk and Progression,” Scientific Reports, vol. 8, pp. 1-12, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Yu Yang, “A Study of Pattern Recognition of Iris Flower based on Machine Learning,” Bachelor’s Thesis, 2013.
[Google Scholar] [Publisher Link]
[21] Anshuman Singh, Rohan Akash, and V. Gokul Rajan, “Flower Classifier Web App Using Ml & Flask Web Framework,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] N. Pavitha, and Shounak Sugave, “A Novel Multi-Stage Stacked Ensemble Classifier using Heterogeneous Base Learners,” International Journal of Engineering Trends and Technology, vol. 71, no. 4, pp. 65-71, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Pranshul Agarwal et al., “Optimised Ensemble Machine Learning Model for IRIS Classification,” 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Adnan Mohsin Abdulazeez et al., “Leaf Identification Based on Shape, Color, Texture and Vines using probabilistic Neural Network,” Computacion y Sistemas, vol. 25, no. 3, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Mohd Arfian Ismail et al., “Newton Competitive Genetic Algorithm Method for Optimization the Production of Biochemical Systems,” Advanced Science Letters, vol. 24, no. 10, pp. 7481-7485, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Mohd Arfian Ismail et al., “Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4-2, pp. 1535-1542, 2017.
[Google Scholar] [Publisher Link]
[27] Mohd Arfian Ismail et al., “Optimisation of Biochemical Systems Production using Hybrid of Newton Method, Differential Evolution Algorithm and Cooperative Coevolution Algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 8, no. 1, pp. 27-35, 2017.
[CrossRef] [Google Scholar] [Publisher Link]