Title: A comparison of imputation methods in the presence of imprecise data when employing a neural network s-Sigmoid function
Authors: Marvin L. Brown, John F. Kros
Addresses: College of Business, Grambling State University, Grambling, LA 71245, USA. ' College of Business, East Carolina University, Greenville, NC 27858, USA
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.
Keywords: knowledge discovery in databases; KDD; data mining; neural networks; imputation; s-Sigmoid function; imprecise data; data missingness.
International Journal of Business Intelligence and Data Mining, 2007 Vol.2 No.3, pp.292 - 310
Available online: 19 Oct 2007 *Full-text access for editors Access for subscribers Purchase this article Comment on this article