TY - GEN
T1 - Investigation into the use of autoencoder neural networks, principal component analysis and support
AU - Marivate, Vukosi Ntsakisi
AU - Nelwamondo, Fulufhelo Vincent
AU - Marwala, Tshilidzi
PY - 2008
Y1 - 2008
N2 - Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal component analysis and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved.
AB - Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal component analysis and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved.
KW - Neural networks in social systems
UR - http://www.scopus.com/inward/record.url?scp=79961018687&partnerID=8YFLogxK
U2 - 10.3182/20080706-5-KR-1001.0988
DO - 10.3182/20080706-5-KR-1001.0988
M3 - Conference contribution
AN - SCOPUS:79961018687
SN - 9783902661005
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
BT - Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
T2 - 17th World Congress, International Federation of Automatic Control, IFAC
Y2 - 6 July 2008 through 11 July 2008
ER -