An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem

IF 2.2 3区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES Symmetry-Basel Pub Date : 2023-11-11 DOI:10.3390/sym15112050
Jelena Mišić, Aleksandar Kemiveš, Milan Ranđelović, Dragan Ranđelović
{"title":"An Asymmetric Ensemble Method for Determining the Importance of Individual Factors of a Univariate Problem","authors":"Jelena Mišić, Aleksandar Kemiveš, Milan Ranđelović, Dragan Ranđelović","doi":"10.3390/sym15112050","DOIUrl":null,"url":null,"abstract":"This study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any process of binary classification. In addition, an ensemble stacking model that involves the asymmetric use of two different well-known algorithms is proposed to determine the importance of individual factors. This model is constructed so that the standard logistic regression is first applied as mandatory. Further, the classification algorithms are implemented if the defined conditions are met. Finally, feature selection algorithms, which belong to the optimization group of algorithms, are applied as a combinatorial algorithm. The proposed model is verified through a case study conducted using real data obtained from health institutions in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model can achieve better results than each of the methods included in it and surpasses several state-of-the-art ensemble algorithms in the field of machine learning. The proposed solution has been implemented in the form of a modern mobile application.","PeriodicalId":48874,"journal":{"name":"Symmetry-Basel","volume":"25 21","pages":"0"},"PeriodicalIF":2.2000,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symmetry-Basel","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/sym15112050","RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

This study proposes an innovative model that determines the importance of selected factors of a univariate problem. The proposed model has been developed based on the example of determining the impact of non-medical factors on the quality of inpatient treatment, but it is generally applicable to any process of binary classification. In addition, an ensemble stacking model that involves the asymmetric use of two different well-known algorithms is proposed to determine the importance of individual factors. This model is constructed so that the standard logistic regression is first applied as mandatory. Further, the classification algorithms are implemented if the defined conditions are met. Finally, feature selection algorithms, which belong to the optimization group of algorithms, are applied as a combinatorial algorithm. The proposed model is verified through a case study conducted using real data obtained from health institutions in the region connected to the city of Nis, Republic of Serbia. The obtained results show that the proposed model can achieve better results than each of the methods included in it and surpasses several state-of-the-art ensemble algorithms in the field of machine learning. The proposed solution has been implemented in the form of a modern mobile application.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
确定单变量问题中单个因素重要性的非对称集成方法
本研究提出了一个创新的模型来确定一个单变量问题的选定因素的重要性。本文提出的模型以确定非医疗因素对住院治疗质量的影响为例,但一般适用于任何二元分类过程。此外,提出了一个集成叠加模型,该模型涉及不对称地使用两种不同的知名算法来确定单个因素的重要性。该模型的构造使得标准逻辑回归首先被强制应用。此外,如果满足定义的条件,则实现分类算法。最后,将特征选择算法作为一种组合算法应用到优化算法中。通过使用从与塞尔维亚共和国尼什市相连的区域的卫生机构获得的真实数据进行的案例研究,对拟议的模型进行了验证。结果表明,该模型比其中的每一种方法都能获得更好的结果,并且超越了机器学习领域中几种最先进的集成算法。所提出的解决方案已以现代移动应用程序的形式实现。
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 去求助
来源期刊
Symmetry-Basel
Symmetry-Basel MULTIDISCIPLINARY SCIENCES-
CiteScore
5.40
自引率
11.10%
发文量
2276
审稿时长
14.88 days
期刊介绍: Symmetry (ISSN 2073-8994), an international and interdisciplinary scientific journal, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided, so that results can be reproduced.
期刊最新文献
The Chromatic Entropy of Linear Supertrees and Its Application Angular-Momentum Modes in a Bosonic Condensate Trapped in the Inverse-Square Potential Development of Compact Bandpass Filter Using Symmetrical Metamaterial Structures for GPS, ISM, Wi-MAX, and WLAN Applications Entanglement and Symmetry Structure of N(= 3) Quantum Oscillators with Disparate Coupling Strengths in a Common Quantum Field Bath Nuclear Shape-Phase Transitions and the Sextic Oscillator
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1