TY - GEN
T1 - Deep learning-bat high-dimensional missing data estimator
AU - Leke, Collins
AU - Ndjiongue, A. R.
AU - Twala, Bhekisipho
AU - Marwala, Tshilidzi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/27
Y1 - 2017/11/27
N2 - In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow Artificial Intelligence frameworks courtesy of their efficiency in low dimensional settings. However, with an increase in dimensionality, these methods yield unrepresentative imputations with an impact on decision making processes. Therefore in this paper, we present a new framework for missing data imputation in high dimensional datasets. A Deep Learning technique is used in conjunction with a swarm intelligence algorithm. The performance of the proposed technique was experimentally tested and compared against other existing methods on an off-line dataset. The results obtained have shown promising potential with slightly longer execution times, which are a worthy tradeoff when accuracy is of importance.
AB - In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow Artificial Intelligence frameworks courtesy of their efficiency in low dimensional settings. However, with an increase in dimensionality, these methods yield unrepresentative imputations with an impact on decision making processes. Therefore in this paper, we present a new framework for missing data imputation in high dimensional datasets. A Deep Learning technique is used in conjunction with a swarm intelligence algorithm. The performance of the proposed technique was experimentally tested and compared against other existing methods on an off-line dataset. The results obtained have shown promising potential with slightly longer execution times, which are a worthy tradeoff when accuracy is of importance.
UR - http://www.scopus.com/inward/record.url?scp=85044433015&partnerID=8YFLogxK
U2 - 10.1109/SMC.2017.8122652
DO - 10.1109/SMC.2017.8122652
M3 - Conference contribution
AN - SCOPUS:85044433015
T3 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
SP - 483
EP - 488
BT - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017
Y2 - 5 October 2017 through 8 October 2017
ER -