基于商业模型的反向传播神经网络算法对电信客户流失的预测与大数据影响分析。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2023-10-01 Epub Date: 2023-01-19 DOI:10.1089/big.2021.0365
Jiabing Xu, Jiarui Liu, Tianen Yao, Yang Li
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引用次数: 1

摘要

本研究旨在将现有的电信运营商从传统的互联网运营商转变为数字驱动的服务,提高电信企业的整体竞争力。数据挖掘应用于电信用户分类,通过数据集成、清理、标准化和转换来处理现有的电信用户数据。现有算法虽然保证了大数据下电信用户分析平台上算法的准确性,但并没有解决单机计算的局限性,也无法有效提高模型的训练效率。为了解决这个问题,本文借助反向传播神经网络(BPNN)算法建立了电信客户流失预测模型,并在Hadoop平台上部署了MapReduce编程框架。本文利用一家电信公司的数据,分析了大数据环境下电信客户的流失情况。研究表明,BPNN中电信客户流失预测模型的准确率为82.12%,部署了大数据集后,模型的学习和训练时间大大缩短。当节点数为8时,模型的加速比保持在60秒。在大数据下,电信用户分析平台不仅保证了算法的准确性,还解决了单机计算的局限性,有效提高了模型的训练效率。与现有研究相比,该模型的精度提高了25.36%,运行时间缩短了约两倍。这种基于BPNN算法的商业模式在处理更多数据集方面具有明显优势,对电信行业数字化驱动的商业模式转型具有很大参考价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction and Big Data Impact Analysis of Telecom Churn by Backpropagation Neural Network Algorithm from the Perspective of Business Model.

This study aims to transform the existing telecom operators from traditional Internet operators to digital-driven services, and improve the overall competitiveness of telecom enterprises. Data mining is applied to telecom user classification to process the existing telecom user data through data integration, cleaning, standardization, and transformation. Although the existing algorithms ensure the accuracy of the algorithm on the telecom user analysis platform under big data, they do not solve the limitations of single machine computing and cannot effectively improve the training efficiency of the model. To solve this problem, this article establishes a telecom customer churn prediction model with the help of backpropagation neural network (BPNN) algorithm, and deploys the MapReduce programming framework on Hadoop platform. Using the data of a telecom company, this article analyzes the loss of telecom customers in the big data environment. The research shows that the accuracy of telecom customer churn prediction model in BPNN is 82.12%. After deploying large data sets, the learning and training time of the model is greatly shortened. When the number of nodes is 8, the acceleration ratio of the model remains at 60 seconds. Under big data, the telecom user analysis platform not only ensures the accuracy of the algorithm, but also solves the limitations of single machine computing and effectively improves the training efficiency of the model. Compared with that of the existing research, the accuracy of the model is improved by 25.36%, and the running time is shortened by about twice. This business model based on BPNN algorithm has obvious advantages in processing more data sets, and has great reference value for the digital-driven business model transformation of the telecommunications industry.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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