Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

IF 1.3 4区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Abdulmajeed Atiah Alharbi
{"title":"Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable","authors":"Abdulmajeed Atiah Alharbi","doi":"10.1155/2024/6671395","DOIUrl":null,"url":null,"abstract":"Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.","PeriodicalId":55177,"journal":{"name":"Discrete Dynamics in Nature and Society","volume":"60 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discrete Dynamics in Nature and Society","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1155/2024/6671395","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.
基于决策树算法的噪声类变量分类性能分析
类噪声是影响分类技术在真实世界数据集上的性能的一个常见问题。当数据集中的类变量具有不正确的类标签时,就会出现类噪声。在有噪声数据的情况下,分类技术对噪声的鲁棒性可能比在无噪声数据集上的性能结果更重要。决策树方法是分类任务中最常用的技术之一。C4.5、CART 和随机森林 (RF) 算法被认为是决策树中最常用的三种算法。本文的目的是就哪种决策树算法在性能和对类噪声的鲁棒性方面更适合用于构建决策树得出结论。为了实现这一目标,我们研究并比较了这些模型在应用于有噪声的类变量时的性能。研究结果表明,与其他算法相比,射频算法对有噪声类变量的数据集更具鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Discrete Dynamics in Nature and Society
Discrete Dynamics in Nature and Society 综合性期刊-数学跨学科应用
CiteScore
3.00
自引率
0.00%
发文量
598
审稿时长
3 months
期刊介绍: The main objective of Discrete Dynamics in Nature and Society is to foster links between basic and applied research relating to discrete dynamics of complex systems encountered in the natural and social sciences. The journal intends to stimulate publications directed to the analyses of computer generated solutions and chaotic in particular, correctness of numerical procedures, chaos synchronization and control, discrete optimization methods among other related topics. The journal provides a channel of communication between scientists and practitioners working in the field of complex systems analysis and will stimulate the development and use of discrete dynamical approach.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信