Missing attribute value prediction based on artificial neural network and rough set theory
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) w...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
2008
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| Subjects: | |
| Online Access: | http://eprints.utp.edu.my/432/1/paper.pdf http://www.scopus.com/inward/record.url?eid=2-s2.0-51549114861&partnerID=40&md5=3efdc016e1106375be3d881f33d1ebb9 http://eprints.utp.edu.my/432/ |
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| Summary: | 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.
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