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Related Experiment Videos

Enhancing prototype reduction schemes with recursion: a method applicable for "large" data sets.

Sang-Woon Kim1, B John Oommen

  • 1Myongji University, Yongin 449-728, Korea. kimsw@mju.ac.kr

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 16, 2004
PubMed
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Adaptive Recursive Partitioning (ARP)_PRS offers a computationally efficient method for prototype reduction. This approach recursively subdivides data, significantly reducing computational burden for large datasets in classification tasks.

Area of Science:

  • Computer Science
  • Machine Learning
  • Data Mining

Background:

  • Prototype Reduction Schemes (PRS) process entire datasets for classification, leading to high computational costs.
  • Existing methods like nearest neighbor classifiers, vector quantization, and support vector machines are computationally intensive.

Purpose of the Study:

  • To introduce a computationally superior mechanism for prototype reduction called Adaptive Recursive Partitioning (ARP)_PRS.
  • To address the excessive computational burden of traditional PRS by processing data in recursively subdivided subsets.

Main Methods:

  • Propose ARP_PRS, a recursive data subdivision strategy that does not rely on clustering.
  • PRS processes smaller data subsets, yielding refined prototypes through iterative refinement.

Related Experiment Videos

  • Eliminates ineffective interior Voronoi space prototypes in subsequent PRS invocations.

    • Main Results:

    • ARP_PRS achieves classification accuracy comparable to state-of-the-art prototype condensation schemes.
    • Demonstrates significant computational advantages, especially for large datasets in data mining and text categorization.
    • Experimental results validate the effectiveness on both artificial and real-life datasets.

    Conclusions:

    • ARP_PRS offers a novel and efficient recursive philosophy for prototype reduction.
    • The method provides a favorable trade-off between computational efficiency and classification accuracy.
    • This recursive strategy is particularly beneficial for large-scale data analysis applications.