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

Ramoni Tirimisiyu Amosa, Adekiigbe Adebanjo, Fabiyi Aderanti Alifat, Olorunlomerue Adam Biodun, Oni Esther Kemi, Adejola Aanu Adeyinka, Adigun Olajide Israel, Joseph Babatunde Isaac
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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.
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基于树的分类器和选择集成学习在鸢尾花分类中的应用研究
-雄辩、希望、知识、有效沟通的能力和信仰是鸢尾花在花的语言中的一些含义。鸢尾有不同的种类类型,每种类型都有自己的药用目的。由于大量的数据集(大数据),对花进行分类已经成为研究人员的一项严肃的任务,因此引入机器学习算法来进行准确可靠的分类。本文重点研究了鸢尾花的五种树分类算法;最佳第一树(BFTree)、最小绝对偏差树(LADTree)、成本敏感决策树(CSForest)、功能树(FT)和随机树(RT)。三种选择的集成学习(Bagging、Dagging和级联泛化)在算法中被平等地实现。本调查中使用的数据集是开源的,可以从公共存储库(kaggle.com)免费下载。分类结果表明,FT分类器的准确率为96.67%,AUC为1.00,优于其他基于树的分类器。集成算法对单分类器(基于树)的性能有显著影响。优于基于树的。采用AUC/ROC(曲线下面积/接收者工作特征)来评价算法的性能。Bagging集合优于其他集合(Dagging和Cascade),准确率为96.00%,AUC为1.00。
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