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Sensitivity Analysis for Probabilistic Neural Network Structure Reduction.

Piotr A Kowalski, Maciej Kusy

    IEEE Transactions on Neural Networks and Learning Systems
    |April 20, 2017
    PubMed
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    Local sensitivity analysis (LSA) offers an effective method for simplifying probabilistic neural networks (PNNs). This approach reduces network complexity by identifying and removing insignificant input features and redundant neurons, enhancing PNN performance.

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Neuroscience

    Background:

    • Probabilistic Neural Networks (PNNs) are powerful classification tools but can suffer from structural complexity.
    • Simplifying PNN structures is crucial for improving computational efficiency and potentially enhancing generalization.
    • Existing reduction methods may not fully address the nuances of PNN architecture.

    Purpose of the Study:

    • To introduce and evaluate Local Sensitivity Analysis (LSA) as a novel method for PNN structure simplification.
    • To develop algorithms for PNN reduction focusing on input layer and pattern neuron simplification.
    • To compare the effectiveness of LSA-based reduction against Global Sensitivity Analysis (GSA) and other common methods.

    Main Methods:

    • Development of three algorithms utilizing LSA for PNN reduction: input layer feature selection, pattern neuron removal, and a combined approach.

    Related Experiment Videos

  • Implementation of PNN with a product kernel estimator and one-dimensional Cauchy function, with dimension-wise smoothing parameter calculation.
  • Testing on eight repository datasets using a 10-fold cross-validation procedure to assess classification accuracy.

    • Main Results:

    • LSA effectively reduces PNN structure by selecting significant input features and removing redundant neurons.
    • Reduced PNNs using LSA demonstrate comparable or improved classification quality compared to full-structure PNNs.
    • LSA-based reduction shows competitive performance against GSA and conventional reduction techniques.

    Conclusions:

    • Local Sensitivity Analysis (LSA) is a viable and effective alternative approach for simplifying probabilistic neural network structures.
    • The proposed LSA algorithms provide a systematic way to optimize PNNs for improved efficiency and performance.
    • This study highlights the potential of sensitivity analysis in refining complex machine learning models.