THE CONCEPT OF INFORMATIVE FEATURES AND METHODS OF THEIR SELECTION

Authors

  • Akhmadjonov M.T. Master’s student in the field of Artificial Intelligence University of Exact and Social Sciences

Abstract

Data processing, classification, and prediction are among the main directions of modern computer science. Solving such tasks often requires selecting only the most important among the many features (attributes) that describe the objects. This process is called informative feature selection.

References

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Guyon, I., & Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157–1182.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.

Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer.

Pedregosa, F., et al. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

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Published

2025-10-03

How to Cite

Akhmadjonov M.T. (2025). THE CONCEPT OF INFORMATIVE FEATURES AND METHODS OF THEIR SELECTION. Ethiopian International Multidisciplinary Research Conferences, 16–18. Retrieved from https://eijmr.org/conferences/index.php/eimrc/article/view/1399

Issue

2025: Ethiopian International Multidisciplinary Research Conferences

Section

Articles

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