Accuracy Comparison of Data Imputation Estimation Methods between the Unconstrained Structural Equation Modeling and K-Nearest Neighbors

Abstract:

This study aimed to the accuracy comparison of data imputation estimation methods between the unconstrained structural equation modeling (Uncon-SEM) and k-nearest neighbors (K-NN). The measurement accuracy of the model based on the mean magnitude of relative error (MMRE). The model is developed by using the online database from University of California, Irvine (UCI) which is a data set on waveform generators. Indicators 21 (1,200 sets) methods were as follows: 1) Data set was divided into two groups (experimental group of 1,000 sets and test group of 200 sets); 2) The experimental group was analyzed by three main factors (F1, F2, F3); 3) Uncon-SEM method: It created a SEM with three main factors, then the remaining factors to be created new the relationships with the unconstrained approach and created new SEM. The test data was substituted in the equation to find the MMRE which was 34.29% (accuracy was 65.71%); 4) K-NN method: It selected the main factor was the relationship of the missing data (F2). Measure the Euclidean distance between the test group and experimental group and selected 5 (K=5) of data sets were nearest to the missing data for the estimate by mean. The MMRE which was 57.00% (accuracy was 43.00%). Thus, comparing estimates of missing data showed that using the Uncon-SEM method were more accuracy, and MMRE declined about 22.71% than K-NN method.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3671-3675
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3671
• Online since: November 2011
• Authors: Narong Phothi, Somchai Prakancharoen
• Keywords: Data Imputation Estimation, K-Nearest Neighbor (KNN), Structural Equation Modeling (SEM), Unconstrained Structural Equation Modeling

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