Sensitivity assessing to data volume for forecasting: introducing similarity methods as suitable ones in feature selection methods

[1] Y. Hmamouche, P. Przymus, A. Casali, and L. Lakhal, GFSM: a feature selection method
for improving time series forecasting, Int. J. Adv. Syst. Meas., (2017).
[2] E. W. Newell and Y. Cheng, Mass cytometry: blessed with the curse of dimensionality,
Nat. Immunol., 17 (2016), pp. 890–895. doi:10.1038/ni.3485.
[3] B. Remeseiro and V. Bolon-Canedo, A review of feature selection methods in medical
applications, Comput. Biol. Med., 112 (2019). doi:10.1016/j.compbiomed.2019.103375.
[4] E. Erguner ¨ Ozko¸c ¨ , Clustering of Time-Series Data, IntechOpen, (2021).
doi:10.5772/intechopen.84490.
[5] A. Alqahtani, M. Ali, X. Xie, and M. W. Jones, Deep Time-Series Clustering: A Review,
Electronics, 10 (23) (2021), 3001. doi:10.3390/electronics10233001.
[6] J. L. Vermeulen, Geometric similarity measures and their applications [dissertation],
Utrecht University, (2023).
[7] H. Xie, J. Li, and H. Xue, A survey of dimensionality reduction techniques based on random
projection, arXiv, (2017). Available from: https://arxiv.org/abs/1706.04371.
[8] X. Zhu, Y. Wang, Y. Li, Y. Tan, G. Wang, and Q. Song, A new unsupervised feature
selection algorithm using similarity-based feature clustering, Comput. Intell., 35 (1) (2019),
pp. 2–22. doi:10.1111/coin.12192.
[9] P. Mitra, C. A. Murthy, and S. K. Pal, Unsupervised feature selection using feature similarity, IEEE Trans. Pattern Anal. Mach. Intell., 24 (3) (2002), pp. 301–312.
doi:10.1109/34.990133.
[10] Q. Yu, S. Jiang, R. Wang, and H. Wang, A feature selection approach based on a similarity
measure for software defect prediction, Front. Inf. Technol. Electron. Eng., 18 (11) (2017),
pp. 1744–1753. doi:10.1631/FITEE.1601322.
[11] Y. Shi, C. Zu, M. Hong, L. Zhou, L. Wang, X. Wu, et al., ASMFS: Adaptive-similaritybased multi-modality feature selection for classification of Alzheimer ’s disease, Pattern Recognit., 126 (2022), 108566. doi:10.1016/j.patcog.2022.108566.
[12] X. Fu, F. Tan, H. Wang, Y. Zhang, and R. W. Harrison, Feature similarity based redundancy reduction for gene selection, In: Proceedings of the International Conference on Data
Mining (Dmin), (2006), pp. 357–360.
[13] A. Vabalas, E. Gowen, E. Poliakoff, and A. J. Casson, Machine learning algorithm
validation with a limited sample size, PLoS One, 14 (11) (2019), e0224365.
[14] G. L. Perry and M. E. Dickson, Using machine learning to predict geomorphic disturbance:
The effects of sample size, sample prevalence, and sampling strategy, J. Geophys. Res. Earth
Surf., 123 (11) (2018), pp. 2954–2970. doi:10.1029/2018JF004640.
[15] Z. Cui and G. Gong, The effect of machine learning regression algorithms and sample size
on individualized behavioral prediction with functional connectivity features, Neuroimage, 178
(2018), pp. 622–637. doi:10.1016/j.neuroimage.2018.06.001.
[16] L. I. Kuncheva, C. E. Matthews, A. Arnaiz-Gonzalez, and J. J. Rodr ´ ´ıguez, Feature
selection from high-dimensional data with very low sample size: A cautionary tale, arXiv,
(2020). Available from: https://arxiv.org/abs/2008.12025.
[17] L. I. Kuncheva and J. J. Rodr´ıguez, On feature selection protocols for very low-sample-size
data, Pattern Recognit., 81 (2018), pp. 660–673. doi:10.1016/j.patcog.2018.03.012.
[18] J. Doak, An evaluation of feature selection methods and their application to computer security [Technical Report], CSE-92-18, (1992).
[19] H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering, IEEE Trans. Knowl. Data Eng., 17 (4) (2005), pp. 491–502.
doi:10.1109/TKDE.2005.66.
[20] C. F. Tsai and Y. T. Sung, Ensemble feature selection in high dimension, low sample
size datasets: Parallel and serial combination approaches, Knowl. Based Syst., 203 (2020),
106097. doi:10.1016/j.knosys.2020.106097.
[21] U. Mori, A. Mendiburu, and J. A. Lozano, Similarity measure selection for clustering time series databases, IEEE Trans. Knowl. Data Eng., 28 (1) (2015), pp. 181–195.
doi:10.1109/TKDE.2015.2462369.
[22] M. Goldani, A review of time series similarity methods, In: Proceedings of the 3rd International Conference on Innovation in Business Management and Economics, (2022).
[23] S. Palkhiwala, M. Shah, and M. Shah, Analysis of machine learning algorithms for predicting a student’s grade, J. Data Inf. Manag., 4 (2022), pp. 329–341. doi:10.1007/s42488-
022-00078-2.
[24] A. C. Rencher and W. F. Christensen, Methods of Multivariate Analysis, 3rd ed., Hoboken: John Wiley & Sons, 2012.