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Abstrakty
In recent years, solar energy forecasting has been increasingly embraced as a sustainablelow-energy solution to environmental awareness. It is a subject of interest to the scientificcommunity, and machine learning techniques have proven to be a powerful means toconstruct an automatic learning model for an accurate prediction. Along with the variousmachine learning and data mining utilities applied to solar energy prediction, the processof feature selection is becoming an ultimate requirement for improving model buildingefficiency. In this paper, we consider the feature selection (FS) approach potential. Weprovide a detailed taxonomy of various feature selection techniques and examine theirusability and ability to deal with a solar energy forecasting problem, given meteorologicaland geographical data. We focus on filter-based, wrapper-based, and embedded-basedfeature selection methods. We use the reduced number of selected features, stability, andregression accuracy and compare feature selection techniques. Moreover, the experimentalresults demonstrate how the feature selection methods studied can considerably improvethe prediction process and how the selected features vary by method, depending on thegiven data constraints.
Rocznik
Tom
Strony
105--118
Opis fizyczny
Bibliogr. 33 poz., tab., wykr.
Twórcy
autor
- Department of Computer Science, Faculty of Science Dhar El Mahraz, University Sidi Mohamed Ben Abdellah, Fez, Morocco
autor
- University Ibn Tofail, Kenitra, Morocco
- Faculty of Environmental Sciences and Biochemistry, University of Castilla-La Mancha,Toledo, Spain
Bibliografia
- 1. M. Diagne, M. David, Ph. Lauret, J. Boland, N. Schmut, Review of solar irradiance forecasting methods and a proposition for small-scale insular grids, Renewable and Sustainable Energy Reviews , 27 : 65-76, 2013, doi: 10.1016/j.rser.2013.06.042.
- 2. H. Liu, L. Yu, Toward integrating feature selection algorithms or classification and clustering, IEEE Trans. on Knowledge and Data Engineering , 17 (4): 491-502, 2005, doi: 10.1109/TKDE.2005.66.
- 3. M.A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, [in:] Proceedings of the Seventeenth International Conference on Machine Learning , ICML ’00, pp. 359-366, Morgan Kaufmann Publishers Inc., 2000.
- 4. M. Dash et al ., Feature selection for clustering – a filter solution, [in:] Proceedings of the 2002 IEEE International Conference on Data Mining , ICDM ’02, pp. 115-122, Washington, DC, USA, IEEE Computer Society, 2002.
- 5. Y. Saeys, I. Inza, P. Larrañaga, A review of feature selection techniques in bioinformatics, Bioinformatics , 23 (19): 2507-2517, 2007 doi: 10.1093/bioinformatics/btm344.
- 6. R. Kohavi, G.H. John, Wrappers for feature subset selection, Artificial Intelligence , 97 (1-2): 273-324, 1997, doi: https://doi.org/10.1016/S0004-3702(97)00043-X.
- 7. L. Rangarajan, Veerabhadrappa. Bi-level dimensionality reduction methods using feature selection and feature extraction, International Journal of Computer Applications , 4 (2): 33-38. 2010.
- 8. I. Guyon, A. Elisseeff, An introduction to variable and feature selection, Journal of Machine Learning Research , 3 : 1157-1182, 2003.
- 9. R. Mundry, C.L. Nunn, Stepwise model fitting and statistical inference: turning noise into signal pollution, The American Naturalist , 173 (1): 119-123, 2009, doi: 10.1086/593303.
- 10. J Reunanen, Overfitting in making comparisons between variable selection methods, Journal of Machine Learning Research , 3 :1371-1382, 2003.
- 11. J. Cai, J. Luo, S. Wang, S. Yang, Feature selection in machine learning: A new perspective, Neurocomputing , 300 : 70-79, 2018, doi: 10.1016/j.neucom.2017.11.077.
- 12. J. Brownlee, Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python , Machine Learning Mastery, 2020.
- 13. J. Li et al. , Feature selection: A data perspective, ACM Computing Surveys , 50 (6): 1-45, 2017, doi: 10.1145/3136625.
- 14. G. Georgiev, I. Valova, N. Gueorguieva, Feature selection for multiclass problems based on information weights, Procedia Computer Science , 6 : 189-194, 2011, doi: 10.1016/j.procs.2011.08.036.
- 15. L. Wang, Y. Wang, Q. Chang, Feature selection methods for big data bioinformatics: A survey from the search perspective, Methods , 111 : 21-31, 2016, doi: 10.1016/ j.ymeth.2016.08.014.
- 16. P. Drotár, J. Gazda, Z. Smékal, An experimental comparison of feature selection methods on two-class biomedical datasets, Computers in Biology and Medicine , 66 : 1-10, 2015, doi: 10.1016/j.compbiomed.2015.08.010.
- 17. S. Khalid, T. Khalil, S. Nasreen, A survey of feature selection and feature extraction techniques in machine learning, [in:] 2014 Science and Information Conference , pp. 372-378, Aug. 2014, doi: 10.1109/SAI.2014.6918213.
- 18. W. Awada, T.M. Khoshgoftaar, D. Dittman, R. Wald, A. Napolitano, A review of the stability of feature selection techniques for bioinformatics data, [in:] 2012 IEEE 13th International Conference on Information Reuse & Integration (IRI) , pp. 356-363, 2012, doi: 10.1109/IRI.2012.6303031.
- 19. R. Martin, R. Aler, J.M. Valls, I.M. Galvan, Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models, Concurrency and Computation: Practice and Experience , 28 (4): 1261–1274, 2016, doi: 10.1002/cpe.3631.
- 20. R. Aler, R. Martín, J.M. Valls, I.M. Galván, A study of machine learning techniques for daily solar energy forecasting using numerical weather models, [in:] D. Camacho, L. Braubach, S. Venticinque, C. Badica [Eds], Intelligent Distributed Computing VIII , Studies in Computational Intelligence , Vol. 570, pp. 269-278, Springer International Publishing, 2015, doi: 10.1007/978-3-319-10422-5_29.
- 21. D. O’Leary, J. Kubby, Feature selection and ANN solar power prediction, Journal of Renewable Energy , 2017 : 1–7, 2017, doi: 10.1155/2017/2437387.
- 22. O. Abedinia, N. Amjady, N. Ghadimi, Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm, Computational Intelligence , 34 (1): 241-260, 2018, doi: 10.1111/coin.12145.
- 23. L. Zhang, J. Wen, A systematic feature selection procedure for short-term data-driven building energy forecasting model development, Energy and Buildings , 183 : 428-442, 2019, doi: 10.1016/j.enbuild.2018.11.010.
- 24. O. Garcia-Hinde et al. , Feature selection in solar radiation prediction using bootstrapped SVRs, [in:] 2016 IEEE Congress on Evolutionary Computation (CEC) , pp. 3638–3645, 2016, doi: 10.1109/CEC.2016.7744250.
- 25. M.R. Hossain, A.M.T. Oo, A.B.M.S. Ali, The effectiveness of feature selection method in solar power prediction, Journal of Renewable Energy , 2013 , Article ID: 952613, 9 pages, 2013, doi: 10.1155/2013/952613.
- 26. C. Lazar et al. , A survey on filter techniques for feature selection in gene expression microarray analysis, IEEE/ACM Transactions on Computational Biology and Bioinformatics , 9 (4): 1106–1119, 2012, doi: 10.1109/TCBB.2012.33.
- 27. A. Kraskov, H. Stögbauer, P. Grassberger, Estimating mutual information, Physical Review E , 69 : 066138, 2004, doi: 10.1103/PhysRevE.69.066138.
- 28. R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological) , 58 (1): 267-288, 1996, doi: 10.1111/j.2517- 6161.1996.tb02080.x.
- 29. L. Breiman, Random Forests, Machine Learning , 45 (1): 5-32, 2001, doi: 10.1023/ A:1010933404324.
- 30. Open Power System Data – A platform for open data of the European power system , https://data.open-power-system-data.org/conventional_power_plants/2018-12-20 (accessed: 2019-09-14).
- 31. A.-C. Haury, P. Gestraud, J.-P. Vert, The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures, PloS ONE , 6 (12): e28210, 2011, doi: 10.1371/journal.pone.0028210.
- 32. P. Mohana Chelvan, K. Perumal, A survey on feature selection stability measures, International Journal of Computer and Information Technology , 5 (1): 98-103, 2016.
- 33. U.M. Khaire, R. Dhanalakshmi, Stability of feature selection algorithm: A review, Journal of King Saud University – Computer and Information Sciences , 2019, doi: 10.1016/j.jksuci.2019.06.012.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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