Abstract
Incomplete data has emerged as a prominent problem in the fields of machine learning, big data and various other academic studies. Due to the surge in deep learning techniques for problem-solving, in this paper, authors have proposed a deep learning-metaheuristic approach to combat the problem of imputing missing data. The proposed approach (DL-GSA) makes use of the nature inspired metaheuristic, Gravitational search algorithm, in combination with a deep-autoencoder and performs better than existing methods in terms of both accuracy and time. Owing to these improvements, DL-GSA has wider applications in both time and accuracy sensitive areas like imputation of scientific and research datasets, data analysis, machine learning and big data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdella, M., Marwala, T.: The use of genetic algorithms and neural networks to approximate missing data in database. In: 2005 IEEE 3rd International Conference on Computational Cybernetics, ICCC 2005, pp. 207–212. IEEE (2005)
Aydilek, I.B., Arslan, A.: A novel hybrid approach to estimating missing values in databases using k-nearest neighbors and neural networks. Int. J. Innovative Comput. Inf. Control 7(8), 4705–4717 (2012)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B (Methodol.) 39, 1–38 (1977)
Enders, C.K.: Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychol. Meth. 8(3), 322 (2003)
Fischer, A., Igel, C.: An introduction to restricted boltzmann machines. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 14–36. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33275-3_2
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Isaacs, J.C.: Representational learning for sonar ATR. In: Proceedings SPIE. vol. 9072, p. 907203 (2014)
LeCun, Y., Cortes, C., Burges, C.J.: Mnist handwritten digit database. AT&T Labs (2010). http://yann.lecun.com/exdb/mnist2
Leke, C., Marwala, T.: Missing data estimation in high-dimensional datasets: a swarm intelligence-deep neural network approach. In: Tan, Y., Shi, Y., Niu, B. (eds.) ICSI 2016. Lecture Notes in Computer Science, vol. 9712. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41000-5_26
Leke, C., Ndjiongue, A.R., Twala, B., Marwala, T.: A deep learning-cuckoo search method for missing data estimation in high-dimensional datasets. In: Tan, Y., Takagi, H., Shi, Y. (eds.) ICSI 2017. LNCS, vol. 10385, pp. 561–572. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61824-1_61
Leke, C., Twala, B., Marwala, T.: Modeling of missing data prediction: computational intelligence and optimization algorithms. In: 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 1400–1404. IEEE (2014)
Little, R.J., Rubin, D.B.: Statistical Analysis With Missing Data. Wiley, New York (2014)
Marivate, V.N., Nelwamodo, F.V., Marwala, T.: Autoencoder, principal component analysis and support vector regression for data imputation. arXiv preprint arXiv:0709.2506 (2007)
Mistry, F.J., Nelwamondo, F.V., Marwala, T.: Missing data estimation using principle component analysis and autoassociative neural networks. J. Syst. Cybern. Inf. 7(3), 72–79 (2009)
Peng, C.Y.J., Harwell, M., Liou, S.M., Ehman, L.H., et al.: Advances in missing data methods and implications for educational research. Real data analysis, pp. 31–78 (2006)
Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)
Rja, L., Rubin, D.: Statistical analysis with missing data. Wiley, New York (1987)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Garg, A., Naryani, D., Aggarwal, G., Aggarwal, S. (2018). DL-GSA: A Deep Learning Metaheuristic Approach to Missing Data Imputation. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-93818-9_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93817-2
Online ISBN: 978-3-319-93818-9
eBook Packages: Computer ScienceComputer Science (R0)