A comparison of imputation methods in the presence of imprecise data when employing a neural network s-Sigmoid function

Abstract

This research addresses the effects of the neural network s-Sigmoid function on Knowledge Discovery of Databases (KDD) in the presence of imprecise data. ANOVA testing and Tukey's Honestly Significant Difference statistics are conducted to investigate the impact of two factors: level of data missingness and imputation method. Data mining is based upon searching the concatenation of multiple databases that usually contain some amount of missing data along with a percentage of inaccurate data and noise. Therefore, analysis depends heavily on the accuracy of the database and on the chosen sample data to be used for model training and testing.