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VGA-Classifier: design and applications.

S Bandyopadhyay1, C A Murthy, S K Pal

  • 1Machine Intelligent Unit, Indian Stat. Inst., Calcutta, India.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces an automated method for evolving the number of hyperplanes in genetic algorithm (GA) classifiers, improving pattern classification accuracy and generalization by preventing overfitting. The approach also aids in determining multilayer perceptron architectures.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Previous genetic algorithm (GA) based pattern classification methods used a fixed number of hyperplanes (H) to approximate class boundaries.
  • This fixed H led to limitations such as overfitting or underfitting, negatively impacting the classifier's generalization capability.

Purpose of the Study:

  • To propose a novel scheme for automatically evolving the number of hyperplanes (H) in GA classifiers.
  • To enhance the generalization capability of GA classifiers by dynamically adjusting H.
  • To describe a method for automatically determining the architecture and connection weights of multilayer perceptrons (MLPs) based on GA principles.

Main Methods:

  • Developed a system using variable length strings/chromosomes to automatically evolve the value of H.

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  • Introduced newly defined crossover and mutation operators to accommodate variable string lengths.
  • Designed a fitness function prioritizing the minimization of misclassified samples and the reduction of hyperplanes.

    • Main Results:

    • The proposed method successfully evolves H, addressing the overfitting/underfitting issues of fixed-H classifiers.
    • The new operators and fitness function effectively manage variable chromosome lengths and optimize hyperplane count.
    • An analogy with multilayer perceptrons (MLPs) enabled automatic determination of MLP architecture and weights.

    Conclusions:

    • Automatically evolving the number of hyperplanes using variable length chromosomes significantly improves GA classifier performance and generalization.
    • The developed techniques offer a robust approach to adaptive classifier design.
    • The established analogy provides a pathway for optimizing MLP architectures and weights through GA-inspired methods.