首页|NMVI: A data-splitting based imputation technique for distinct types of missing data
NMVI: A data-splitting based imputation technique for distinct types of missing data
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NSTL
Elsevier
? 2022 Elsevier B.V.In the IoT world, where minute digital devices are acclimated to sense the data, a failure in such devices results in immense information loss and insufficient information regarding datasets results in inappropriate decisions. Missing values within a dataset have an adverse effect on the data analysis. Data Analysts in pre-processing phase perform data imputation before analyzing the dataset. Distinct traditional methods, which are predicated on simple techniques (mean, case deletion, mode or median), show poor performance while estimating the missing values. In this paper, a novel splitting-based Nullify the Missing Values before Imputation (NMVI) is proposed in which the data is first split into complete and incomplete subsets and then an upper-limit is set for every class having missing data that assists the model to estimate missing values closer to the exact values. The proposed NMVI technique surmounts the constraint of exiting imputation techniques that are completely dependent on complete variables within a class to estimate the missing values. The proposed NMVI technique has comparatively less computational time because of which it is beneficial for real-time quandaries. The experimental results depict that the proposed NMVI technique estimates the missing values in an efficient manner with respect to RMSEs, Adjusted coefficient of determination, Accuracy and Correlation coefficient irrespective of the dimensionality as well as missing rate within a dataset.
Data imputationData missingnessIncomplete datasetsMissing value imputationMissingness mechanisms
Bhagat H.V.、Singh M.
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Sant Longowal Institute of Engineering and Technology
NSTL
Elsevier
