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Related Experiment Videos

A Lempel-Ziv complexity-based neural network pruning algorithm.

Sultan Uddin Ahmed1, Md Shahjahan, Kazuyuki Murase

  • 1Department of Electronics and Communication Engineering, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh. sultan_ahmed001@yahoo.com

International Journal of Neural Systems
|September 30, 2011
PubMed
Summary

This study introduces the Silent Pruning Algorithm (SPA), a novel method for simplifying artificial neural networks (ANNs) by pruning hidden units based on Lempel-Ziv complexity (LZC). SPA enhances network generalization while minimizing training disruption.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Artificial Neural Networks (ANNs) are powerful tools but can become overly complex.
  • Pruning methods aim to simplify ANNs by removing redundant components.
  • Existing pruning techniques may cause significant disturbance during network training.

Purpose of the Study:

  • To introduce a novel pruning method for ANNs called the Silent Pruning Algorithm (SPA).
  • To leverage Lempel-Ziv complexity (LZC) for identifying and pruning redundant hidden units.
  • To develop a pruning method that minimizes disturbance during the training process.

Main Methods:

  • The Silent Pruning Algorithm (SPA) utilizes Lempel-Ziv complexity (LZC) to rank hidden units.
  • LZC quantifies the number of unique patterns in a hidden unit's output time sequence; lower LZC indicates higher redundancy.
  • Hidden units are pruned based on their LZC ranks during the ANN training process.

Main Results:

  • SPA was tested on benchmark machine learning datasets (cancer, diabetes, heart, etc.).
  • SPA demonstrated superior performance compared to the Random Deletion Algorithm (RDA).
  • Experimental results indicate that SPA effectively simplifies ANNs while maintaining good generalization ability.

Conclusions:

  • The Silent Pruning Algorithm (SPA) offers an effective approach to prune ANNs using Lempel-Ziv complexity (LZC).
  • SPA achieves network simplification and improved generalization with minimal training disturbance.
  • The method shows promise for developing more efficient and biologically inspired artificial neural networks.