Abstract
Ant-lion optimizer (ALO) algorithm is also a population-based meta-heuristic algorithm capable of finding approximate solutions to complex optimization problems. In this chapter, we present another new framework for missing data imputation in the high-dimensional dataset. A deep autoencoder is used in conjunction with the ALO algorithm (DL-ALO). The performance of the proposed technique is experimentally tested and compared against other existing methods of a similar nature using an off-line handwritten digits image recognition dataset. The results obtained are in line with those from previous chapters, further emphasizing the effectiveness and applicability of a deep learning framework in the domain being considered. Although the model portrays slightly longer execution times, which are a worthy trade-off when accuracy is of importance in real-world applications, it is important to further consider such frameworks.
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References
Abdella, M., & Marwala, T. (2005). The use of genetic algorithms and neural networks to approximate missing data in database (vol. 24, pp. 577–589).
Aydilek, I., & Arslan, A. (2012). A novel hybrid approach to estimating missing values in databases using k-nearest neighbors and neural networks. International Journal of Innovative Computing, Information and Control, 7(8), 4705–4717.
Baraldi, A., & Enders, C. (2010). An introduction to modern missing data analyses. Journal of School Psychology, 48(1), 5–37.
Carter, R. L. (2006). Solutions for missing data in structural equation modelling. Research & Practice in Assessment, 1(1), 1–6.
Gupta, E., & Saxena, A. (2016). Performance evaluation of antlion optimizer based regulator in automatic generation control of interconnected power system. Journal of Engineering, 2016, 1–14.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.
Jerez, J. M., Molina, I., Garcia-Laencina, P. J., Alba, E., Ribelles, N., Martin, M., et al. (2010). Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artificial Intelligence in Medicine, 50(2), 105–115.
Leke, C., Twala, B., & Marwala, T. (2014). Modeling of missing data prediction: Computational intelligence and optimization algorithms. In IEEE International Conference on Systems, Man and Cybernetics (SMC) (pp. 1400–1404). San Diego, CA, USA.
Lobato, F., Sales, C., Araujo, I., Tadaiesky, V., Dias, L., Ramos, L., & Santana, A. (2015). Multi-objective genetic algorithm for missing data imputation. Pattern Recognition Letters, 68(1), 126–131, Retrieved March 18, 2016.
Marivate, V. N., Nelwamondo, F. V., & Marwala, T. (2007). Autoencoder, principal component analysis and support vector regression for data imputation. arXiv:0709.2506.
Mirjalili, S. (2015). The ant lion optimizer. Advances in Engineering Software, 8, 80–98.
Mistry, F. J., Nelwamondo, F. V., & Marwala, T. (2009). Missing data estimation using principle component analysis and autoassociative neural networks. Journal of Systemics, Cybernatics and Informatics, 7(3), 72–79.
Mohamed, A. K., Nelwamondo, F. V., & Marwala, T. (2007). Estimating missing data using neural network techniques, principal component analysis and genetic algorithms. In Proceedings of the Eighteenth Annual Symposium of the Pattern Recognition Association of South Africa.
Nelwamondo, F. V., Mohamed, S., & Marwala, T. (2007). Missing data: A comparison of neural network and expectation maximisation techniques. Current Science, 93(12), 1514–1521.
Petrovi, M., Petronijevi, J., Miti, M., Vukovi, N., Plemi, A., Miljkovi, Z., et al. (2015). The ant lion optimization algorithm for flexible process planning. Journal of Production Engineering, 18(2), 65–68.
Rana, S., John, A. H., Midi, H., & Imon, A. (2015). Robust regression imputation for missing data in the presence of outliers. Far East Journal of Mathematical Sciences, 97(2), 183–195.
Satheeshkumar, R., & Shivakumar, R. (2016). Ant lion optimization approach for load frequency control of multi-area interconnected power systems. Circuits and Systems, 7, 2357–2383.
Sidekerskiene, T., & Damasevicius, R. (2016). Reconstruction of missing data in synthetic time series using EMD. CEUR Workshop Proceedings, 1712, 7–12.
Van Buuren, S. (2012). Flexible imputation of missing data. CRC press.
Yamany, W., Tharwat, A., Fawzy, Gaber, T., & Hassanien, A. E. (2015). A new multilayer perceptrons trainer based on ant lion optimization algorithm. In Fourth International Conference on Information Science and Industrial Applications (ISI) (pp. 40–45).
Zainuri, N. A., Jemain, A. A., & Muda, N. (2015). A comparison of various imputation methods for missing values in air quality data. Sains Malaysiana, 44(3), 449–456.
Zhang, S., Jin, Z., & Zhu, X. (2011). Missing data imputation by utilizing information within incomplete instances. Journal of Systems and Software, 84(3), 452–459.
Zhang, S. (2011). Shell-neighbor method and its application in missing data imputation. Applied Intelligence, 35(1), 123–133.
Zawbaa, H. M., Emary, E., & Grosan, C. (2016). Feature selection via chaotic antlion optimization. PLOS ONE, 11(3),1–21. Retrieved June 2016, from https://doi.org/10.1371/journal.pone.0150652.
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Leke, C.A., Marwala, T. (2019). Missing Data Estimation Using Ant-Lion Optimizer Algorithm. In: Deep Learning and Missing Data in Engineering Systems. Studies in Big Data, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-01180-2_7
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DOI: https://doi.org/10.1007/978-3-030-01180-2_7
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