随机森林分类的可视化

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Catarina Maçãs, João R Campos, Nuno Lourenço, Penousal Machado
{"title":"随机森林分类的可视化","authors":"Catarina Maçãs, João R Campos, Nuno Lourenço, Penousal Machado","doi":"10.1177/14738716241260745","DOIUrl":null,"url":null,"abstract":"Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.","PeriodicalId":50360,"journal":{"name":"Information Visualization","volume":"4 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Visualisation of Random Forest classification\",\"authors\":\"Catarina Maçãs, João R Campos, Nuno Lourenço, Penousal Machado\",\"doi\":\"10.1177/14738716241260745\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.\",\"PeriodicalId\":50360,\"journal\":{\"name\":\"Information Visualization\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Visualization\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/14738716241260745\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Visualization","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/14738716241260745","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

摘要

在有监督的机器学习算法中,决策树(DTs)是最普遍的选择。这些算法形成二进制结构,根据不同的规则有效地将数据划分为更小的部分。因此,DT 树可作为一种学习机制,用于识别数据集中所有元素的最佳分离和分类规则。由于 DTs 类似于基于规则的决策,因此很容易解释。此外,DTs 对数据预处理的需求极低,而且在处理各种数据类型方面具有多功能性,因此在不同领域都具有很强的实用性和用户友好性。然而,当面对大量数据集或涉及多棵树的集合(如随机森林)时,其分析可能会变得具有挑战性。为了便于检查和验证这些模型,我们开发了一种可视化工具,其中包含一系列可视化内容,既能提供一组 DT 的概览,也能提供详细的洞察。我们的工具旨在为相同的数据提供不同的视角,从而加深对决策过程的理解。本文概述了我们的设计方法,介绍了各种可视化模型,并详细介绍了迭代验证过程。我们通过一个电信用例来验证我们的方法,特别是利用可视化工具来解读基于 DT 的模型如何确定电信运营商在联系客户时使用的最佳通信渠道(即电话、电子邮件、短信)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualisation of Random Forest classification
Decision Trees (DTs) stand out as a prevalent choice among supervised Machine Learning algorithms. These algorithms form binary structures, effectively dividing data into smaller segments based on distinct rules. Consequently, DTs serve as a learning mechanism to identify optimal rules for the separation and classification of all elements within a dataset. Due to their resemblance to rule-based decisions, DTs are easy to interpret. Additionally, their minimal need for data pre-processing and versatility in handling various data types make DTs highly practical and user-friendly across diverse domains. Nevertheless, when confronted with extensive datasets or ensembles involving multiple trees, such as Random Forests, its analysis can become challenging. To facilitate the examination and validation of these models, we have developed a visual tool that incorporates a range of visualisations providing both an overview and detailed insights into a set of DTs. Our tool is designed to offer diverse perspectives on the same data, enabling a deeper understanding of the decision-making process. This article outlines our design approach, introduces various visualisation models, and details the iterative validation process. We validate our methodology through a telecommunications use case, specifically employing the visual tool to decipher how a DT-based model determines the optimal communication channel (i.e. phone call, email, SMS) for a telecommunication operator to use when contacting a client.
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
Information Visualization
Information Visualization COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
5.40
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Information Visualization is essential reading for researchers and practitioners of information visualization and is of interest to computer scientists and data analysts working on related specialisms. This journal is an international, peer-reviewed journal publishing articles on fundamental research and applications of information visualization. The journal acts as a dedicated forum for the theories, methodologies, techniques and evaluations of information visualization and its applications. The journal is a core vehicle for developing a generic research agenda for the field by identifying and developing the unique and significant aspects of information visualization. Emphasis is placed on interdisciplinary material and on the close connection between theory and practice. This journal is a member of the Committee on Publication Ethics (COPE).
×
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