SIGNIFICANCE OF SELECTION ALGORITHMS IN DATA PROCESSING

Authors

Ma’mura Gayratovna Ergasheva Pedagogue, Department of general technical sciences Asia International University

Keywords:

Selection algorithms, training set reduction, data preprocessing, feature selection, classification, nearest neighbor, distance metric, data mining, machine learning, data optimization

Abstract

This work focuses on the application of selection algorithms for reducing training datasets in classification tasks. The method analyzes the structure of data using distance-based measures and identifies less informative or redundant objects for removal. As a result, a more compact and efficient dataset is obtained, which improves computational performance and supports accurate model learning.

Downloads

Download data is not yet available.

References

Trevor Hastie, Robert Tibshirani, Jerome Friedman. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction.” Springer, 2009.

Christopher M. Bishop. “Pattern Recognition and Machine Learning.” Springer, 2006.

Wes McKinney. “Data Structures for Statistical Computing in Python.” Proceedings of the 9th Python in Science Conference, 2010.

Richard O. Duda, Peter E. Hart, David G. Stork. “Pattern Classification.” Wiley-Interscience, 2001.

Tom M. Mitchell. “Machine Learning.” McGraw-Hill, 1997.

Ian Goodfellow, Yoshua Bengio, Aaron Courville. “Deep Learning.” MIT Press, 2016.

Kevin P. Murphy. “Machine Learning: A Probabilistic Perspective.” MIT Press, 2012.

Ethem Alpaydin. “Introduction to Machine Learning.” MIT Press, 2020.

Downloads

Published

2026-04-23

How to Cite

Ma’mura Gayratovna Ergasheva. (2026). SIGNIFICANCE OF SELECTION ALGORITHMS IN DATA PROCESSING. International Multidisciplinary Journal for Research & Development, 13(4), 1964–1968. Retrieved from https://ijmrd.in/index.php/imjrd/article/view/5977