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
Affiliations:
College of Business, Grambling State University, Grambling, LA 71245, USA.;College of Business, East Carolina University, Greenville, NC 27858, USA
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2007

Citing 15
Cited 0

Statistical analysis with missing data

Statistical analysis with missing data
Multivariate data analysis (4th ed.): with readings

Multivariate data analysis (4th ed.): with readings
Analysis and applications of artificial neural networks

Analysis and applications of artificial neural networks
Data mining

Data mining
Data mining: building competitive advantage

Data mining: building competitive advantage
Neural, Novel and Hybrid Algorithms for Time Series Prediction

Neural, Novel and Hybrid Algorithms for Time Series Prediction
Machine Learning

Machine Learning
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Collateral missing value imputation: a new robust missing value estimation algorithm for microarray data

Bioinformatics
Improving the prediction performance of customer behavior through multiple imputation

Intelligent Data Analysis
Data mining from 1994 to 2004: an application-orientated review

International Journal of Business Intelligence and Data Mining
Data dimensionality reduction with application to improving classification performance and explaining concepts of data sets

International Journal of Business Intelligence and Data Mining
Utilising neural network applications to enhance efficiency in the healthcare industry: predicting populations of future chronic illness

International Journal of Business Intelligence and Data Mining
A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.