Abstract
For predictive analysis, Independent features and feature combination are of equal importance, but most models only focus on either independent features or feature combinations. In this paper, we propose a novel deep network model for predictive analysis. It incorporates two components: wide simple feed-forward neural network and MLP (multilayer perceptron) neural network. The wide simple feed-forward neural network is used to generalize to unseen feature combinations, and MLP neural network’s aim to select and memorize vital independent features. The Feed-forward & MLP models are jointly trained for the Feed-forward & MLP model, in order to combine the benefits of selection, memorization and generalization. The results from the experiments show the jointly trained Neural Networks model can achieve ideal accuracy.
This work is supported in part by Key Laboratory Open Project Fund of Engineering and Technical Research Center of Embankment Safety and Disease Control of Ministry of Water Resources (2018007), National Key R&D Program of China (2018YFB1201403) and CERNET Innovation Project (NGII20180702).
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She, W., Xu, L., Xu, H., Zhang, X., Hu, Y., Tian, Z. (2020). Multilayer Perceptron Based on Joint Training for Predicting Popularity. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_50
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