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Some novel classifiers designed using prototypes extracted by a new scheme based on self-organizing feature map.

A Laha1, N R Pal

  • 1Nat. Inst. of Manage. Calcutta.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
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We introduce two novel schemes for designing prototype-based classifiers, addressing prototype number, generation, and utilization. The 1-most similar prototype (1-MSP) classifier demonstrates improved performance over the 1-nearest multiple prototype (1-NMP) classifier.

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Mining

Background:

  • Prototype-based classification is a key area in machine learning.
  • Existing methods face challenges in managing prototype quantity, generation, and effective utilization.
  • Variations in data variance across classes can hinder classifier performance.

Purpose of the Study:

  • To propose two comprehensive schemes for designing effective prototype-based classifiers.
  • To address critical aspects: number, generation, and utilization of prototypes.
  • To develop classifiers that handle variations in data variance more effectively.

Main Methods:

  • Utilizing Kohonen's self-organizing feature map (SOFM) for initial prototype generation.
  • Employing a dynamic prototype generation and tuning algorithm (DYNAGEN) for refinement.

Related Experiment Videos

  • Designing two classifiers: 1-nearest multiple prototype (1-NMP) and 1-most similar prototype (1-MSP).
  • The 1-MSP classifier incorporates zones of influence and Euclidean-norm similarity for enhanced performance.

    • Main Results:

    • The SOFM-based DYNAGEN algorithm efficiently generates an optimal number of prototypes.
    • The 1-NMP classifier shows good performance but struggles with high variance data.
    • The 1-MSP classifier consistently outperforms the 1-NMP classifier, especially with varying data variances.
    • Comparative analysis against benchmark results confirms the proposed classifiers' efficacy.

    Conclusions:

    • The proposed comprehensive schemes offer robust solutions for designing prototype-based classifiers.
    • The 1-MSP classifier, with its zones of influence, effectively addresses challenges posed by data with large variance variations.
    • These novel approaches represent a significant advancement in prototype-based classification techniques.