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Prototype selection for nearest neighbor classification: taxonomy and empirical study.

Salvador García1, Joaquín Derrac, José Ramón Cano

  • 1University of Jaén, Campus las Lagunillas S/N, Jaén, spain. sglopez@ujaen.es

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Summary
This summary is machine-generated.

Prototype selection methods reduce training data for nearest neighbor classification, improving efficiency and accuracy. This study surveys these methods, offering a new taxonomy and empirical performance analysis.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Nearest neighbor (NN) classification is widely used but has drawbacks like high storage needs and low efficiency.
  • Prototype selection aims to mitigate these issues by reducing the training dataset size.

Purpose of the Study:

  • To survey and categorize prototype selection methods for NN classification.
  • To provide a theoretical taxonomy and empirical performance evaluation of these methods.

Main Methods:

  • A theoretical review to establish a taxonomy of prototype selection techniques.
  • An empirical study comparing method performance on various datasets regarding accuracy, data reduction, and runtime.

Main Results:

  • A novel taxonomy categorizing prototype selection methods based on their core characteristics.
  • Empirical evidence demonstrating the trade-offs between accuracy, data reduction, and runtime for different methods.

Conclusions:

  • Prototype selection offers a promising solution to NN classifier limitations.
  • The study provides guidelines and recommendations for selecting appropriate methods based on specific application needs.