Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things
This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons,...
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| Veröffentlicht in: | Mathematical problems in engineering 2015-01, Vol.2015 (2015), p.1-8 |
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| creator | Wang, Feng Hu, Liang Xiong, Weiqing Yan, Xiaobo Zhao, Kuo |
| description | This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively. |
| doi_str_mv | 10.1155/2015/548605 |
| format | Article |
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Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2015/548605</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Algorithms ; Clustering ; Computer science ; Data analysis ; Data processing ; Experiments ; Gaussian ; Grants ; Health care ; Internet ; Internet of Things ; Logistics ; Mathematical models ; Missing data ; Mixtures ; Probabilistic models ; Reliability ; Sensors ; Transportation ; Wireless networks</subject><ispartof>Mathematical problems in engineering, 2015-01, Vol.2015 (2015), p.1-8</ispartof><rights>Copyright © 2015 Xiaobo Yan et al.</rights><rights>Copyright © 2015 Xiaobo Yan et al. Xiaobo Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c455t-8e11e6bc4ce21963992eb9f6ac1e8b3c5608394428efa24034f6d637f56828d53</citedby><cites>FETCH-LOGICAL-c455t-8e11e6bc4ce21963992eb9f6ac1e8b3c5608394428efa24034f6d637f56828d53</cites><orcidid>0000-0001-5584-7442 ; 0000-0003-0412-7554 ; 0000-0001-8540-5557 ; 0000-0003-4465-9854</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Hanne, Thomas</contributor><creatorcontrib>Wang, Feng</creatorcontrib><creatorcontrib>Hu, Liang</creatorcontrib><creatorcontrib>Xiong, Weiqing</creatorcontrib><creatorcontrib>Yan, Xiaobo</creatorcontrib><creatorcontrib>Zhao, Kuo</creatorcontrib><title>Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things</title><title>Mathematical problems in engineering</title><description>This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Clustering</subject><subject>Computer science</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Experiments</subject><subject>Gaussian</subject><subject>Grants</subject><subject>Health care</subject><subject>Internet</subject><subject>Internet of Things</subject><subject>Logistics</subject><subject>Mathematical models</subject><subject>Missing data</subject><subject>Mixtures</subject><subject>Probabilistic models</subject><subject>Reliability</subject><subject>Sensors</subject><subject>Transportation</subject><subject>Wireless networks</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0E1Lw0AQBuBFFKzVk3dZ8CJK7M5-ZXPUorXQ4sEq3sI2mdiUNKm7Ceq_d0s8iBdPM4dnhpmXkFNg1wBKjTgDNVLSaKb2yACUFpECGe-HnnEZARevh-TI-zVjHBSYAXmal96X9Rt9sVWHdLrZdq1ty6amt9ZjTkMzsV0gtqbz8rPtHNJ5k2NFi8bRdhVG6hZdjS1tCrpYhVX-mBwUtvJ48lOH5Pn-bjF-iGaPk-n4ZhZlUqk2MgiAepnJDDkkWiQJx2VSaJsBmqXIlGZGJFJyg4XlkglZ6FyLuFDacJMrMSQX_d6ta9479G26KX2GVWVrbDqfgjYqDhZkoOd_6LrpXB2uC0oLmRgmk6CuepW5xnuHRbp15ca6rxRYugs43QWc9gEHfdnr8HRuP8p_8FmPMZDwzy8cS-AgvgErn4Ip</recordid><startdate>20150101</startdate><enddate>20150101</enddate><creator>Wang, Feng</creator><creator>Hu, Liang</creator><creator>Xiong, Weiqing</creator><creator>Yan, Xiaobo</creator><creator>Zhao, Kuo</creator><general>Hindawi Publishing Corporation</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7SC</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5584-7442</orcidid><orcidid>https://orcid.org/0000-0003-0412-7554</orcidid><orcidid>https://orcid.org/0000-0001-8540-5557</orcidid><orcidid>https://orcid.org/0000-0003-4465-9854</orcidid></search><sort><creationdate>20150101</creationdate><title>Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things</title><author>Wang, Feng ; 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Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). 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| subjects | Accuracy Algorithms Clustering Computer science Data analysis Data processing Experiments Gaussian Grants Health care Internet Internet of Things Logistics Mathematical models Missing data Mixtures Probabilistic models Reliability Sensors Transportation Wireless networks |
| title | Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things |
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