Feature Selection for Imputation of Missing Data to Measure the Efficiency of Nile Tilapia Production in Suphanburi Province
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Abstract
The objectives of this research were: 1) to development of a new method (FSNNR) for imputation
of missing data that combined feature selection with the nearest neighbor regression imputation
method, 2) to compare the efficiency of FSNNR and three imputations (RI, KNN and NNR) under
36 situations from these three conditions; the sample sizes; the missing percentages; and the data
deviations. The data were simulated using the Monte Carlo technique; repeated 1,000 times for
each situation, and 3) to measure the efficiency of Nile tilapia production in Suphanburi Province.
The results were as followed:
1) The new method for imputation of missing data was as follows:
First, we select two features of data using the Nearest Neighbor. Next, we impute the missing
value using the K-Nearest Neighbor Imputation (k=2). Finally, complete the data obtained from
step 2 to impute missing data with Regression Imputation.
2) The FSNNR was better performed than the other imputations under 33 combinations of
simulated conditions.
3) The technical efficiency of each Nile Tilapia farmer when replacing missing data with FSNNR
method, it was found that the Nile Tilapia farmers have the highest level of technical efficiency
82.76% and high technical efficiency of 24.14%.
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