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International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 8, August 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



OPTIMIZATION FOR PREDICTING MISSING DATA IN DATABASE TRANSFER PROCESSING

[Full Text]

 

AUTHOR(S)

SUMITRA NUANMEESRI

 

KEYWORDS

CROSS-VALIDATION, MISSING DATA, OPTIMIZATION, RANDOM FOREST, RESAMPLE, SMOTE

 

ABSTRACT

THE OBJECTIVE OF THE ARTICLE IS TO OPTIMIZING DATA FOR PREDICTING AND FILLING THE MISSING DATA IN THE PROCESS OF DATABASE TRANSFER FROM SEVERAL DATABASES TO A CENTRAL DATABASE OR THE NEW DATABASE SYSTEM. THE RESEARCH RESULT SHOWS THAT THE RESAMPLE TECHNIQUE CAN IMPROVE THE DATASET FROM 3,190 TO 29,800 RECORDS, WHILE THE SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE GAINS THE DATASET UP TO16,563 RECORDS, WHICH GENERATED AT 1000% OF THE ORIGINAL DATASET. WHEN CREATING A MODEL TO PREDICTING THE MISSING DATA IN DATABASE TRANSFER PROCESS WITH THE RANDOM FOREST TECHNIQUE, IT WAS FOUND THAT THE EFFICIENCY OF THE MODEL EVALUATION BY USING THE 10-FOLD CROSS-VALIDATION METHOD GAVE THE MODEL ACCURACY OF THE SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE THAT APPROACH TO THE HIGHER THAN RESAMPLE METHOD IN EVERY DATA RANGE. IT WILL BE ABLE TO CLASSIFY THE DATA TO REPRESENT THE MISSING DATA DURING THE DATABASE TRANSFER PROCESS WITH MORE THAN 96% EFFICIENCY.

 

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