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The goal of Feature Selection - comprising filter, wrapper, and embedded approaches - is to find the optimal feature subset for designated downstream tasks.
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R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial intelligence , vol. 97, no. 1-2, pp. 273–324, 1997
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M. A. Hall, “Feature selection for discrete and numeric class machine learning,” 1999
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Y. Kim, W. N. Street, and F. Menczer, “Feature selection in unsupervised learning via evolutionary search,” in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , 2000, pp. 365–369
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G. Forman et al. , “An extensive empirical study of feature selection metrics for text classification.” J. Mach. Learn. Res. , vol. 3, no. Mar, pp. 1289–1305, 2003
2003
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L. Yu and H. Liu, “Feature selection for high-dimensional data: A fast correlation-based filter solution,” in Proceedings of the 20th international conference on machine learning (ICML-03) , 2003, pp. 856–863
2003
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H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on pattern analysis and machine intelligence , vol. 27, no. 8, pp. 1226–1238, 2005
2005
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P. M. Granitto, C. Furlanello, F. Biasioli, and F. Gasperi, “Recursive feature elimination with random forest for ptr-ms analysis of agroindustrial products,” Chemometrics and intelligent laboratory systems , vol. 83, no. 2, pp. 83–90, 2006
2006
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V. Sugumaran, V. Muralidharan, and K. Ramachandran, “Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing,” Mechanical systems and signal processing , vol. 21, no. 2, pp. 930–942, 2007
2007
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J. Biesiada and W. Duch, “Feature selection for high-dimensional data—a pearson redundancy based filter,” in Computer recognition systems 2 . Springer, 2008, pp. 242–249
2008
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2014
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B. Seijo-Pardo, V. Bolón-Canedo, and A. Alonso-Betanzos, “On developing an automatic threshold applied to feature selection ensembles,” Information Fusion , vol. 45, pp. 227–245, 2019
2019
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W. Fan, K. Liu, H. Liu, P. Wang, Y. Ge, and Y. Fu, “Autofs: Automated feature selection via diversity-aware interactive reinforcement learning,” in 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 2020, pp. 1008–1013
2020
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I. Lemhadri, F. Ruan, and R. Tibshirani, “Lassonet: Neural networks with feature sparsity,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 10–18
2021
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K. Liu, P. Wang, D. Wang, W. Du, D. O. Wu, and Y. Fu, “Efficient reinforced feature selection via early stopping traverse strategy,” in 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 2021, pp. 399–408
2021
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H. Ding, P.-M. Feng, W. Chen, and H. Lin, “Identification of bacteriophage virion proteins by the anova feature selection and analysis,” Molecular BioSystems , vol. 10, no. 8, pp. 2229–2235, 2014
2014
Cited alongside, same era.
M. N. Haque, N. Noman, R. Berretta, and P. Moscato, “Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data classification,” PloS one , vol. 11, no. 1, p. e0146116, 2016
2016
Cited alongside, same era.
B. Seijo-Pardo, I. Porto-Díaz, V. Bolón-Canedo, and A. Alonso-Betanzos, “Ensemble feature selection: homogeneous and heterogeneous approaches,” Knowledge-Based Systems , vol. 118, pp. 124–139, 2017
2017
Cited alongside, same era.
J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu, “Feature selection: A data perspective,” ACM Computing Surveys (CSUR) , vol. 50, no. 6, pp. 1–45, 2017
2017
Cited alongside, same era.
B. Seijo-Pardo, V. Bolón-Canedo, and A. Alonso-Betanzos, “Testing different ensemble configurations for feature selection,” Neural Processing Letters , vol. 46, no. 3, pp. 857–880, 2017
2017
Cited alongside, same era.
B. Pes, N. Dessì, and M. Angioni, “Exploiting the ensemble paradigm for stable feature selection: a case study on high-dimensional genomic data,” Information Fusion , vol. 35, pp. 132–147, 2017
2017
Cited alongside, same era.
K. Liu, Y. Fu, P. Wang, L. Wu, R. Bo, and X. Li, “Automating feature subspace exploration via multi-agent reinforcement learning,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2019, pp. 207–215
2019
Cited alongside, same era.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
W. Fan, K. Liu, H. Liu, A. Hariri, D. Dou, and Y. Fu, “Autogfs: Automated group-based feature selection via interactive reinforcement learning,” in Proceedings of the 2021 SIAM International Conference on Data Mining (SDM) . SIAM, 2021, pp. 342–350
2021
Later among the works it cites.
A. Hashemi, M. B. Dowlatshahi, and H. Nezamabadi-pour, “Ensemble of feature selection algorithms: a multi-criteria decision-making approach,” International Journal of Machine Learning and Cybernetics , vol. 13, no. 1, pp. 49–69, 2022
2022
Later among the works it cites.
D. Wang, Y. Fu, K. Liu, X. Li, and Y. Solihin, “Group-wise reinforcement feature generation for optimal and explainable representation space reconstruction,” Proceedings of the 28th ACM SIGKDD international conference on Knowledge discovery and data mining , 2022
2022
Later among the works it cites.
A. Kumagai, T. Iwata, Y. Ida, and Y. Fujiwara, “Few-shot learning for feature selection with hilbert-schmidt independence criterion,” Advances in Neural Information Processing Systems , vol. 35, pp. 9577–9590, 2022
2022
Later among the works it cites.
K. Koyama, K. Kiritoshi, T. Okawachi, and T. Izumitani, “Effective nonlinear feature selection method based on hsic lasso and with variational inference,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2022, pp. 10 407–10 421
2022
Later among the works it cites.
M. G. Altarabichi, S. Nowaczyk, S. Pashami, and P. S. Mashhadi, “Fast genetic algorithm for feature selection—a qualitative approximation approach,” Expert systems with applications , vol. 211, p. 118528, 2023
2023
Closest in time.
M. Xiao, D. Wang, M. Wu, Z. Qiao, P. Wang, K. Liu, Y. Zhou, and Y. Fu, “Traceable automatic feature transformation via cascading actor-critic agents,” in Proceedings of the 2023 SIAM International Conference on Data Mining (SDM) . SIAM, 2023, pp. 775–783
2023
Closest in time.
M. Xiao, D. Wang, M. Wu, K. Liu, H. Xiong, Y. Zhou, and Y. Fu, “Traceable group-wise self-optimizing feature transformation learning: A dual optimization perspective,” 2023
2023
Closest in time.