CIRA:类不平衡弹性自适应高斯过程分类器

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Salma Abdelmonem, Dina Elreedy, Samir I. Shaheen
{"title":"CIRA:类不平衡弹性自适应高斯过程分类器","authors":"Salma Abdelmonem,&nbsp;Dina Elreedy,&nbsp;Samir I. Shaheen","doi":"10.1016/j.knosys.2024.112500","DOIUrl":null,"url":null,"abstract":"<div><p>The problem of class imbalance is pervasive across various real-world applications, resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm-level balancing approaches adapt the machine learning algorithms to learn from imbalanced datasets while preserving the data’s original distribution. The Gaussian process classifier is a powerful machine learning classification algorithm, however, as with other standard classifiers, its classification performance could be exacerbated by class imbalance. In this work, we propose the Class Imbalance Resilient Adaptive Gaussian process classifier (CIRA), an algorithm-level adaptation of the binary Gaussian process classifier to alleviate the class imbalance. To the best of our knowledge, the proposed algorithm (CIRA) is the first adaptive method for the Gaussian process classifier to handle unbalanced data. The proposed CIRA algorithm consists of two balancing modifications to the original classifier. The first modification balances the posterior mean approximation to learn a more balanced decision boundary between the majority and minority classes. The second modification adopts an asymmetric conditional prediction model to give more emphasis to the minority points during the training process. We conduct extensive experiments and statistical significance tests on forty-two real-world unbalanced datasets. Through the experiments, our proposed CIRA algorithm surpasses six popular data sampling methods with an average of 2.29%, 3.25%, 3.67%, and 1.81% in terms of the Geometric mean, F1-measure, Matthew correlation coefficient, and Area under the receiver operating characteristics curve performance metrics respectively.</p></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CIRA: Class imbalance resilient adaptive Gaussian process classifier\",\"authors\":\"Salma Abdelmonem,&nbsp;Dina Elreedy,&nbsp;Samir I. Shaheen\",\"doi\":\"10.1016/j.knosys.2024.112500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The problem of class imbalance is pervasive across various real-world applications, resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm-level balancing approaches adapt the machine learning algorithms to learn from imbalanced datasets while preserving the data’s original distribution. The Gaussian process classifier is a powerful machine learning classification algorithm, however, as with other standard classifiers, its classification performance could be exacerbated by class imbalance. In this work, we propose the Class Imbalance Resilient Adaptive Gaussian process classifier (CIRA), an algorithm-level adaptation of the binary Gaussian process classifier to alleviate the class imbalance. To the best of our knowledge, the proposed algorithm (CIRA) is the first adaptive method for the Gaussian process classifier to handle unbalanced data. The proposed CIRA algorithm consists of two balancing modifications to the original classifier. The first modification balances the posterior mean approximation to learn a more balanced decision boundary between the majority and minority classes. The second modification adopts an asymmetric conditional prediction model to give more emphasis to the minority points during the training process. We conduct extensive experiments and statistical significance tests on forty-two real-world unbalanced datasets. Through the experiments, our proposed CIRA algorithm surpasses six popular data sampling methods with an average of 2.29%, 3.25%, 3.67%, and 1.81% in terms of the Geometric mean, F1-measure, Matthew correlation coefficient, and Area under the receiver operating characteristics curve performance metrics respectively.</p></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124011341\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124011341","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

类不平衡问题在现实世界的各种应用中普遍存在,导致机器学习分类器偏向于多数类。算法级平衡方法可以调整机器学习算法,以便从不平衡的数据集中学习,同时保持数据的原始分布。高斯过程分类器是一种功能强大的机器学习分类算法,然而,与其他标准分类器一样,它的分类性能可能会因类别不平衡而恶化。在这项工作中,我们提出了类不平衡弹性自适应高斯过程分类器(CIRA),它是二进制高斯过程分类器的一种算法级自适应,可以缓解类不平衡问题。据我们所知,所提出的算法(CIRA)是高斯过程分类器处理不平衡数据的第一种自适应方法。所提出的 CIRA 算法包括对原始分类器的两个平衡修改。第一种修改是平衡后验均值近似值,以便在多数类和少数类之间学习更平衡的决策边界。第二项修改采用了非对称条件预测模型,在训练过程中更加重视少数群体点。我们在 42 个真实世界的非平衡数据集上进行了广泛的实验和统计显著性测试。通过实验,我们提出的 CIRA 算法在几何平均数、F1-measure、马太相关系数和接收者工作特征曲线下面积等性能指标上分别以 2.29%、3.25%、3.67% 和 1.81% 的平均值超过了六种流行的数据抽样方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CIRA: Class imbalance resilient adaptive Gaussian process classifier

The problem of class imbalance is pervasive across various real-world applications, resulting in machine learning classifiers exhibiting bias towards majority classes. Algorithm-level balancing approaches adapt the machine learning algorithms to learn from imbalanced datasets while preserving the data’s original distribution. The Gaussian process classifier is a powerful machine learning classification algorithm, however, as with other standard classifiers, its classification performance could be exacerbated by class imbalance. In this work, we propose the Class Imbalance Resilient Adaptive Gaussian process classifier (CIRA), an algorithm-level adaptation of the binary Gaussian process classifier to alleviate the class imbalance. To the best of our knowledge, the proposed algorithm (CIRA) is the first adaptive method for the Gaussian process classifier to handle unbalanced data. The proposed CIRA algorithm consists of two balancing modifications to the original classifier. The first modification balances the posterior mean approximation to learn a more balanced decision boundary between the majority and minority classes. The second modification adopts an asymmetric conditional prediction model to give more emphasis to the minority points during the training process. We conduct extensive experiments and statistical significance tests on forty-two real-world unbalanced datasets. Through the experiments, our proposed CIRA algorithm surpasses six popular data sampling methods with an average of 2.29%, 3.25%, 3.67%, and 1.81% in terms of the Geometric mean, F1-measure, Matthew correlation coefficient, and Area under the receiver operating characteristics curve performance metrics respectively.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
×
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