Application of random forest in big data completion

Telecom operators have a lot of data, but in view of a variety of reasons, the quality of the data is not ideal, there are a lot of data is not complete or even missing. For existing data mining, it is necessary to carry out the data to meet the quality of the data and to achieve sufficient sampling...

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Main Authors: Zheng WANG, Hua REN, Yanping FANG
Format: Article
Language:zho
Published: Beijing Xintong Media Co., Ltd 2016-12-01
Series:Dianxin kexue
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Online Access:http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016317/
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author Zheng WANG
Hua REN
Yanping FANG
author_facet Zheng WANG
Hua REN
Yanping FANG
author_sort Zheng WANG
collection DOAJ
description Telecom operators have a lot of data, but in view of a variety of reasons, the quality of the data is not ideal, there are a lot of data is not complete or even missing. For existing data mining, it is necessary to carry out the data to meet the quality of the data and to achieve sufficient sampling proportion. Relying on the country's existing log retention system, template library design data integrity, authentication could not meet the quality requirements of the data, using the random forest algorithm, the same data with or related data was found, data was completed and data quality was improved, and the template library was extended by optimization of feedback. The construction of completion data subsystem in the system log retained end-to-end data quality guaranteed and improved quality, completed and improved the real-time data and historical data, and ultimately met the requirements of data processing and mining operators, improved data quality and value.
format Article
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institution Kabale University
issn 1000-0801
language zho
publishDate 2016-12-01
publisher Beijing Xintong Media Co., Ltd
record_format Article
series Dianxin kexue
spelling doaj-art-8b93b8b5af8244d0b2a08cadffdad6cf2025-01-15T03:13:38ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012016-12-013271259605313Application of random forest in big data completionZheng WANGHua RENYanping FANGTelecom operators have a lot of data, but in view of a variety of reasons, the quality of the data is not ideal, there are a lot of data is not complete or even missing. For existing data mining, it is necessary to carry out the data to meet the quality of the data and to achieve sufficient sampling proportion. Relying on the country's existing log retention system, template library design data integrity, authentication could not meet the quality requirements of the data, using the random forest algorithm, the same data with or related data was found, data was completed and data quality was improved, and the template library was extended by optimization of feedback. The construction of completion data subsystem in the system log retained end-to-end data quality guaranteed and improved quality, completed and improved the real-time data and historical data, and ultimately met the requirements of data processing and mining operators, improved data quality and value.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016317/big datarandom forestmachine learningdata completion
spellingShingle Zheng WANG
Hua REN
Yanping FANG
Application of random forest in big data completion
Dianxin kexue
big data
random forest
machine learning
data completion
title Application of random forest in big data completion
title_full Application of random forest in big data completion
title_fullStr Application of random forest in big data completion
title_full_unstemmed Application of random forest in big data completion
title_short Application of random forest in big data completion
title_sort application of random forest in big data completion
topic big data
random forest
machine learning
data completion
url http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016317/
work_keys_str_mv AT zhengwang applicationofrandomforestinbigdatacompletion
AT huaren applicationofrandomforestinbigdatacompletion
AT yanpingfang applicationofrandomforestinbigdatacompletion