电信行业使用可解释性和AutoML的集成客户分析

Sai Tejashwin Eswarapu, Sesharhri S, Yashwanth Deshaboina, Bhargawa P., Ashly Ann Jo, Ebin Deni Raj
{"title":"电信行业使用可解释性和AutoML的集成客户分析","authors":"Sai Tejashwin Eswarapu, Sesharhri S, Yashwanth Deshaboina, Bhargawa P., Ashly Ann Jo, Ebin Deni Raj","doi":"10.1109/ICAAIC56838.2023.10141019","DOIUrl":null,"url":null,"abstract":"This research study provides an outlook on modeling an integrated customer analytic framework using complex black-box AutoML pipelines while providing insights and explanations to the predictions provided to the customer data mainly in two use cases: Customer Churn and Customer Segmentation. Upon the Literature Review conducted, a pipeline has been derived to integrate both the use cases using supervised and unsupervised models, and explanations were obtained using XAI techniques. The experiments were conducted on the model with desired results and fairness checks were done to check the integrity of the predictions and explanations. The purpose of this research study is to automate the customer analysis process with a comparatively better performance without building a manual pipeline from scratch.","PeriodicalId":267906,"journal":{"name":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrated Customer Analytics using Explainability and AutoML for Telecommunications\",\"authors\":\"Sai Tejashwin Eswarapu, Sesharhri S, Yashwanth Deshaboina, Bhargawa P., Ashly Ann Jo, Ebin Deni Raj\",\"doi\":\"10.1109/ICAAIC56838.2023.10141019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This research study provides an outlook on modeling an integrated customer analytic framework using complex black-box AutoML pipelines while providing insights and explanations to the predictions provided to the customer data mainly in two use cases: Customer Churn and Customer Segmentation. Upon the Literature Review conducted, a pipeline has been derived to integrate both the use cases using supervised and unsupervised models, and explanations were obtained using XAI techniques. The experiments were conducted on the model with desired results and fairness checks were done to check the integrity of the predictions and explanations. The purpose of this research study is to automate the customer analysis process with a comparatively better performance without building a manual pipeline from scratch.\",\"PeriodicalId\":267906,\"journal\":{\"name\":\"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAAIC56838.2023.10141019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAAIC56838.2023.10141019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本研究提供了一个使用复杂的黑盒自动化管道建模集成客户分析框架的前景,同时提供了主要在两个用例中提供给客户数据的预测的见解和解释:客户流失和客户细分。在进行文献综述之后,已经衍生出一个管道,使用监督和非监督模型集成用例,并使用XAI技术获得解释。在得到预期结果的模型上进行了实验,并进行了公平性检查,以检查预测和解释的完整性。本研究的目的是使客户分析过程自动化,从而获得相对更好的性能,而无需从头开始构建人工管道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Integrated Customer Analytics using Explainability and AutoML for Telecommunications
This research study provides an outlook on modeling an integrated customer analytic framework using complex black-box AutoML pipelines while providing insights and explanations to the predictions provided to the customer data mainly in two use cases: Customer Churn and Customer Segmentation. Upon the Literature Review conducted, a pipeline has been derived to integrate both the use cases using supervised and unsupervised models, and explanations were obtained using XAI techniques. The experiments were conducted on the model with desired results and fairness checks were done to check the integrity of the predictions and explanations. The purpose of this research study is to automate the customer analysis process with a comparatively better performance without building a manual pipeline from scratch.
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
自引率
0.00%
发文量
0
期刊最新文献
Mosquitoes Classification using EfficientNetB4 Transfer Learning Model A Novel Framework in Scheduling Packets for Energy-Efficient Bandwidth Allocation in Wireless Networks Malware Classification using Malware Visualization and Deep Learning Automatic Vehicle Classification and Speed Tracking Predicting and Analyzing Air Quality Features Effectively using a Hybrid Machine Learning Model
×
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