eprintid: 443
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/04/43
datestamp: 2023-11-09 15:16:05
lastmod: 2023-11-09 15:16:05
status_changed: 2023-11-09 15:14:34
type: conference_item
metadata_visibility: show
creators_name: Setiawan, N.A.
creators_name: Venkatachalam, P.A.
creators_name: Hani, A.F.M.
title: Missing attribute value prediction based on artificial neural network and rough set theory
ispublished: pub
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
note: cited By 27; Conference of BioMedical Engineering and Informatics: New Development and the Future - 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008 ; Conference Date: 27 May 2008 Through 30 May 2008; Conference Code:73399
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.
date: 2008
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-51549114861&doi=10.1109%2fBMEI.2008.322&partnerID=40&md5=732c623827796e11f6d0dea06548d400
id_number: 10.1109/BMEI.2008.322
full_text_status: none
publication: BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008
volume: 1
place_of_pub: Sanya, Hainan
pagerange: 306-310
refereed: TRUE
isbn: 9780769531182
citation:   Setiawan, N.A. and Venkatachalam, P.A. and Hani, A.F.M.  (2008) Missing attribute value prediction based on artificial neural network and rough set theory.  In: UNSPECIFIED.