SIGNIFICANCE OF SELECTION ALGORITHMS IN DATA PROCESSING
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.
Keywords
Selection algorithms, training set reduction, data preprocessing, feature selection, classification, nearest neighbor, distance metric, data mining, machine learning, data optimization
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