@inproceedings{scholars443,
           title = {Missing attribute value prediction based on artificial neural network and rough set theory},
         address = {Sanya, Hainan},
          volume = {1},
            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},
             doi = {10.1109/BMEI.2008.322},
         journal = {BioMedical Engineering and Informatics: New Development and the Future - Proceedings of the 1st International Conference on BioMedical Engineering and Informatics, BMEI 2008},
           pages = {306--310},
            year = {2008},
        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. {\^A}{\copyright} 2008 IEEE.},
        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},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-51549114861&doi=10.1109\%2fBMEI.2008.322&partnerID=40&md5=732c623827796e11f6d0dea06548d400},
            isbn = {9780769531182},
          author = {Setiawan, N. A. and Venkatachalam, P. A. and Hani, A. F. M.}
}