AI-Based Automated Feature Selection for Predictive Modeling

  • Authors

    • Dr. Liu Fang Associate Professor, Tsinghua University, China. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2023PII8X5W

    Published 10-03-2023

  • Artificial Intelligence, Automated Feature Selection, Predictive Modeling, Machine Learning, Genetic Algorithm, Particle Swarm Optimization, Classification, Dimensionality Reduction, Hybrid Optimization, Data Mining, Evolutionary Computing, Intelligent Systems, Feature Engineering, Support Vector Machine, Neural Networks

    Issue

    Vol. 6 No. 2 (2023)

    Section

    Articles

    How to Cite

    [1]
    L. Fang, “AI-Based Automated Feature Selection for Predictive Modeling”, IJMLPA, vol. 6, no. 2, pp. 01–19, Oct. 2023, doi: 10.67228/3142788X/IJMLPA-2023PII8X5W.

  • Abstract

    Artificial Intelligence (AI) has improved predictive modeling by enabling efficient analysis of large-scale and high-dimensional datasets. Before 2019, selecting relevant features from complex data was a major challenge. Feature selection is important for improving accuracy, reducing computational cost, preventing overfitting, and enhancing model interpretability. Traditional methods such as filter, wrapper, and embedded approaches had limitations with big data and nonlinear relationships. To address these issues, AI-based Automated Feature Selection (AFS) techniques were introduced. These methods combine machine learning, optimization algorithms, and intelligent search strategies to automatically identify the most relevant features. Common approaches included Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), and Reinforcement Learning (RL). This study reviews AI-driven feature selection techniques developed between 2009 and 2018 and proposes a hybrid framework combining filter-based preprocessing, evolutionary optimization, and machine learning evaluation. Experimental results show that AI-based hybrid methods outperform traditional techniques in accuracy, scalability, and feature optimization, demonstrating their importance in modern predictive analytics systems.

  • References

    [1] I. Guyon and A. Elisseeff, “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.

    [2] H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Boston, MA, USA: Springer, 1998.

    [3] M. Dash and H. Liu, “Feature Selection for Classification,” Intelligent Data Analysis, vol. 1, no. 3, pp. 131–156, 1997.

    [4] R. Kohavi and G. H. John, “Wrappers for Feature Subset Selection,” Artificial Intelligence, vol. 97, no. 1–2, pp. 273–324, 1997.

    [5] P. Langley, “Selection of Relevant Features in Machine Learning,” in Proceedings of the AAAI Fall Symposium on Relevance, New Orleans, LA, USA, 1994, pp. 140–144.

    [6] 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), Washington, DC, USA, 2003, pp. 856–863.

    [7] 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, Aug. 2005.

    [8] I. Kononenko, “Estimating Attributes: Analysis and Extensions of Relief,” in Proceedings of the European Conference on Machine Learning, Catania, Italy, 1994, pp. 171–182.

    [9] J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” in Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, 1995, pp. 1942–1948.

    [10] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA, USA: Addison-Wesley, 1989.

    [11] M. Dorigo and T. Stützle, Ant Colony Optimization. Cambridge, MA, USA: MIT Press, 2004.

    [12] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

    [13] V. Vapnik, The Nature of Statistical Learning Theory. New York, NY, USA: Springer, 1995.

    [14] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene Selection for Cancer Classification Using Support Vector Machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002.

    [15] Y. LeCun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

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