Selection of Characteristics by Hybrid Method: RFE, Ridge, Lasso, and Bayesian for the Power Forecast for a Photovoltaic System
Author:
Cruz JoseORCID, Mamani WilsonORCID, Romero ChristianORCID, Pineda FerdinandORCID
Publisher
Springer Singapore
Reference11 articles.
1. Çürük, E., Acı, Ç., Saraç Eşsiz, E.: The effects of attribute selection in artificial neural network based classifiers on cyberbullying detection. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, pp. 6–11 (2018). https://doi.org/10.1109/UBMK.2018.8566312 2. Wu, Q., Zhang, H., Jing, R., Li, Y.: Feature selection based on twin support vector regression. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, pp. 2903–2907 (2019). https://doi.org/10.1109/SSCI44817.2019.9003001 3. Zheng, Z., Cai, Y., Yang, Y., Li, Y.: Sparse weighted Naive Bayes classifier for efficient classification of categorical data. In: IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, pp. 691–696 (2018). https://doi.org/10.1109/DSC.2018.00110 4. Al-Fahad, R., Yeasin, M., Anam, A.I., Elahian, B.: Selection of stable features for modeling 4-D affective space from EEG recording. In: International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, pp. 1202–1209 (2017). https://doi.org/10.1109/IJCNN.2017.7965989 5. Tanizaka Filho, M.O., Marujo, E.C., dos Santos, T.C.: Identification of features for profit forecasting of soccer matches. In: 8th Brazilian Conference on Intelligent Systems (BRACIS), Salvador, Brazil, pp. 18–23 (2019). https://doi.org/10.1109/BRACIS.2019.00013
|
|