Abstract—In this study, a method based on different feature engineering / feature extraction / feature derivation is proposed
for improving air passenger forecasting by machine learning existing libraries. In this kind of formulation, we kept focus on
creating different kinds of datasets that differ one from another
by methodology so we extracted new features and compared new feature space with original feature space in terms of variable importance. We conducted experiments to improve the variance by aggregating all the features in final feature space. Finally, we optimized a deep learning model to have a Robust Neural Network Topology.
Index Terms—Deep learning, logistic regression, feature extraction, neural network.
Riaz Ullah Khan, Rajesh Kumar, and Xiaosong Zhang are with school of Computer Science, University of Electronic Science and Technology of China, China (e-mail: rerukhan@ gmail.com, rajakumarlohano@gmail.com, johnsonzxs@ uestc.edu.cn).
Nawsher khan is with Abdul Wali Khan University Mardan, Pakistan and King Khalid University Abha, Kingdom of Saudi Arabia (e-mail: nawsherkhan@ gmail.com).
Ijaz Ahad is with School of Information and Software Engineering, University of Electronic Science and Technology of China, China (e-mail: ijazahad1@ gmail.com).
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Cite: Riaz Ullah Khan, Rajesh Kumar, Nawsher Khan, Xiaosong Zhang, and Ijaz Ahad, "Optimizing a Deep Learning Model in Order to Have a Robust Neural Network Topology," International Journal of Modeling and Optimization vol. 8, no. 3, pp. 页码, 2018.
