2021
DOI: 10.1016/j.micpro.2020.103636
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Imputation using information fusion technique for sensor generated incomplete data with high missing gap

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Cited by 11 publications
(3 citation statements)
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“…The managed and flexible federation, processing, and analysis of data from the different distribution sources are referred to as data integration [ 7 ]. Data integration is crucial as data preprocessing [ 8 ] and data mining [ 9 , 10 ] for exploiting the value of large and distributed datasets that are available today. Distributed processing infrastructures such as Cloud, Grids, and peer-to-peer networks can be used for data integration on geographically distributed sites.…”
Section: Introductionmentioning
confidence: 99%
“…The managed and flexible federation, processing, and analysis of data from the different distribution sources are referred to as data integration [ 7 ]. Data integration is crucial as data preprocessing [ 8 ] and data mining [ 9 , 10 ] for exploiting the value of large and distributed datasets that are available today. Distributed processing infrastructures such as Cloud, Grids, and peer-to-peer networks can be used for data integration on geographically distributed sites.…”
Section: Introductionmentioning
confidence: 99%
“…Using a modified random forest algorithm based on the decision pathway to compensate for the effects of incomplete data, Yuequn Zhang et al [3]. D Adhikari et al use information fusion technology for interpolation, and use the ratio-based interpolation (RBI) algorithm to deal with high rates of missing data based on data fusion and data mining methods [4]. Lai X et al used an autoencoder (AE) -based multi-task learning (MTL) model and dynamically optimized missing values to classify incomplete data sets with interdependencies among properties [5].…”
Section: Introductionmentioning
confidence: 99%
“…Missing data is unavoidable, despite the fact that we are all aware that gathering as much data as possible is the ideal strategy for data analysis. Although it is commonly known that erasing missing information is simple and quick, several studies have come to the conclusion that this approach does not work in all situations [5,6]. For instance, removing will result in the loss of some important data when the missing value is not entirely random [7,8].…”
Section: Introductionmentioning
confidence: 99%