Geographic Context-Based Stacking Learning for Election Prediction from Socio-economic Data
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-21686-2_44
Reference14 articles.
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3. Graefe, A., Green, K.C., Armstrong, J.S.: Accuracy gains from conservative forecasting: Tests using variations of 19 econometric models to predict 154 elections in 10 countries. PLoS ONE 14(1), e0209850 (2019)
4. Jacintho, L.H.M., Silva, T.P., Parmezan, A.R.S., Batista, G.E.A.P.A.: Analysing spatio-temporal voting patterns in Brazilian elections through a simple data science pipeline. J. Inf. Data Manag. 1–16 (2021)
5. Jiang, Z., Sainju, A.M., Li, Y., Shekhar, S., Knight, J.: Spatial ensemble learning for heterogeneous geographic data with class ambiguity. ACM Trans. Intell. Syst. Technol. 10(4), 1–25 (2019)
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