会议论文详细信息
1st International Conference on Environmental Geography and Geography Education
Modeling na?ve bayes imputation classification for missing data
生态环境科学;地球科学
Khotimah, B.K.^1^3 ; Miswanto^1^2 ; Suprajitno, H.^1^2
Faculty of Science and Technology, University of Airlangga, Surabaya, Indonesia^1
Department of Mathematics, University of Airlangga, Surabaya, Indonesia^2
Departmentof Informatic Engineering, University of Trunojoyo Madura, Bangkalan, Indonesia^3
关键词: Attribute information;    Classification process;    Imputation algorithm;    Imputation methods;    Imputation process;    Multiple imputation;    Probability estimate;    Supervised classification;   
Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/243/1/012111/pdf
DOI  :  10.1088/1755-1315/243/1/012111
Subject:57.1
来源: IOP
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【 摘 要 】
Naïve Bayes Imputation (NBI) is used to fill in missing values by replacing the attribute information according to the probability estimate. The NBI process divides the whole data into two sub-sets is the complete data and data containing missing data. Complete data is used for the imputation process at the lost value. The process is repeated for each missing attribute to generate complete data for classification. This research applies NBI for imputation and preprocessing as preparation of classification process. The trial of this study used NBI for imputation compared to using the mean and mode to predict the missing data. The data used for imputation is full train of complete data as a whole to predict the missing value so as to represent the entire data. The results of this study prove that imputation with NBI produces the right imputation with higher accuracy than other imputations. NBI with single imputation and multiple imputation results in better performance because of the right features. This study aims to calculate the effect of missing values on Naïve Bayes Imputation Algorithm is based on a probalistic model using mixed data. Empirically shows that the interaction between several methods of imputation and supervised classification results in differences in the performance of classification for the same imputation method.
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