Evolutionary Feature Selection for Time-Series Forecasting
Authors: 
Maria Lourdes Linares-Barrera, Manuel J. Jimenez-Navarro, Isabel Sofia Brito, 
José C. Riquelme, María Martínez-BallesterosAuthors Info & Claims
Maria Lourdes Linares-Barrera, Manuel J. Jimenez-Navarro, Isabel Sofia Brito, José C. Riquelme, María Martínez-BallesterosAuthors Info & ClaimsPages 395 - 399
Abstract
In machine learning, feature selection is crucial for pinpointing the key subset of features that enhances interpretability and preserves or boosts the model's original performance. Filter methods, which assess features using statistical metrics, are particularly notable. Recently, a novel metric called Conditional Dependence Coefficient has been proposed to measure the dependence between subsets of variables.
This paper introduces a novel filter feature selection method that integrates the Conditional Dependence Coefficient metric with an evolutionary algorithm to find the optimal feature subset. This approach combines the adaptability of genetic algorithms with the strength of an intuitive metric. Unlike many filter-based methods, our technique does not rely on parameters directly linked to the number of features (like thresholds). Moreover, it evaluates the collective merit of feature subsets rather than individual significance.
We conducted tests on six different multivariate time-series datasets to address the forecasting challenge, determining the relevant lags. Considering no selection as baseline, experimental results indicate that our approach is competitive in terms of efficacy while demonstrating a reduction in the number of features selected.
References
[1]
M. Azadkia and S. Chatterjee. 2021. A simple measure of conditional dependence. The Annals of Statistics 49, 6 (2021), 3070--3102.
[2]
V. Bolón-Canedo, A. Alonso-Betanzos, L. Morán-Fernández, and B. Cancela. 2022. Feature selection: From the past to the future. In Proceedings of Advances in Selected Artificial Intelligence Areas: World Outstanding Women in Artificial Intelligence. Springer, 11--34.
[3]
CDT. 2015. California department of transportation.
[4]
J. Dunn, L. Mingardi, and Y.D. Zhuo. 2021. Comparing interpretability and explainability for feature selection.
[5]
F. J. Galán-Sales, P. Reina-Jiménez, M. Carranza-García, and J. M. Luna-Romera. 2023. An Approach to Enhance Time Series Forecasting by Fast Fourier Transform. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer, 259--268.
[6]
R. Godahewa, C. Bergmeir, G. Webb, R. Hyndman, and P. Montero-Manso. 2020. Electricity Hourly Dataset.
[7]
A. Gómez-Losada, G. Asencio-Cortés, F. Martínez-Álvarez, and J.C. Riquelme. 2018. A novel approach to forecast urban surface-level ozone considering heterogeneous locations and limited information. Environmental Modelling & Software 110 (2018), 52--61.
[8]
M.J. Jiménez-Navarro, C. Restrepo-Estrada, L. Melgar-García, and D. Gutierrez-Aviles. 2023. Feature Selection Guided by CVOA Metaheuristic for Deep Neural Networks: Application to Multivariate Time Series Forecasting. In Proceedings of 18th International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer, 209--218.
[9]
M. J. Jiménez-Navarro, M. Martínez-Ballesteros, I. Brito, F. Martínez-Álvarez, and G. Cortés. 2022. Feature-Aware Drop Layer (FADL): A Nonparametric Neural Network Layer for Feature Selection. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. Springer, 557--566.
[10]
A. Khemphila and V. Boonjing. 2011. Heart Disease Classification Using Neural Network and Feature Selection. Proceedings of the International Conference on Systems Engineering 21 (2011), 406--409.
[11]
M. L. Linares-Barrera, M. Martínez-Ballesteros, J. M. García-Heredia, and J. C. Riquelme. 2023. A Feature Selection and Association Rule Approach to Identify Genes Associated with Metastasis and Low Survival in Sarcoma. In Proceedings of the International Conference on Hybrid Artificial Intelligence Systems. Springer, 731--742.
[12]
J. Miao and L. Niu. 2016. A survey on feature selection. Procedia computer science 91 (2016), 919--926.
[13]
T. Niu, J. Li, W. Wei, and H. Yue. 2022. A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting. Applied Energy 326 (2022), 119964.
[14]
M. Rong, D. Gong, and X. Gao. 2019. Feature selection and its use in big data: challenges, methods, and trends. IEEE Access 7 (2019), 19709--19725.
[15]
S. Velliangiri and S. Alagumuthukrishnan. 2019. A review of dimensionality reduction techniques for efficient computation. Procedia Computer Science 165 (2019), 104--111.
[16]
J. Wu, X. Chen, H. Zhang, L. Xiong, H. Lei, and S. Deng. 2019. Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology 17, 1 (2019), 26--40.
Index Terms
- Evolutionary Feature Selection for Time-Series Forecasting
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April 2024
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Published: 21 May 2024
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