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Missing attribute value prediction based on artificial neural network and rough set theory

A.F.M., Hani and N.A., Setiawan and P.A., Venkatachalam (2008) Missing attribute value prediction based on artificial neural network and rough set theory. In: BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008, 27 May 2008 through 30 May 2008, Sanya, Hainan.

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Abstract

In this research, artificial neural network (ANN) combined with rough set theory (RST), named as ANNRST, is proposed to predict missing values of attribute. The prediction of missing values of attribute is applied on heart disease data from UCI datasets. The ANN used is multilayer perceptron (MLP) with resilient back-propagation learning. RST can reduce the dimensionality of attributes through its reduct. Reduct is used as input of ANN combined with decision attribute. By simulating of missing values, the prediction accuracy of ANN is compared to ANNRST. The accuracy of ANNRST is also compared with missing data imputation of k-Nearest Neighbor (k-NN), most common attribute value method and ANN with piecewise linear network-orthonormal least square feature selection (PLN-OLS). Simulation results show that ANNRST can predict the missing value with maximum accuracy close to ANN without dimensionality reduction (pure ANN) and outperform k-NN, most common attribute value method, and ANN with PLN-OLS. © 2008 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Autocorrelation; Biomedical engineering; Biophysics; Computer networks; Curve fitting; Forecasting; Fuzzy sets; Image classification; Least squares approximations; Multilayer neural networks; Neural networks; Piecewise linear techniques; Set theory; Missing value; Missing values; Neural network; Rough set theory
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Departments / MOR / COE:Departments > Electrical & Electronic Engineering
ID Code:432
Deposited By: Prof Ir Dr Ahmad Fadzil Mohd Hani
Deposited On:09 Mar 2010 01:59
Last Modified:19 Jan 2017 08:26

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