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Design and Analysis for Fall Detection System Simplification
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Model Simplification of Deep Random Forest for Real-Time Applications of Various Sensor Data.

Sangwon Kim1, Byoung-Chul Ko1, Jaeyeal Nam1

  • 1Department of Computer Engineering, Keimyung University, Daegu 42601, Korea.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

This study introduces a novel method to simplify deep random forests (DRF) by eliminating redundant rules. The simplified DRF models maintain high performance while improving transparency and efficiency.

Keywords:
deep random forestinterpretable machine learningmodel simplificationrule eliminationtransparent machine learning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep Random Forests (DRF) offer high performance comparable to Deep Neural Networks (DNNs) without backpropagation.
  • However, the complex structure of DRF, with numerous interconnected decision trees, hinders model interpretability and analysis.

Purpose of the Study:

  • To propose a new method for simplifying black-box DRF models.
  • To enhance the transparency and efficiency of DRF models through rule elimination.

Main Methods:

  • Quantifying feature contributions and rule frequency within a trained DRF decision rule set.
  • Implementing a rule elimination strategy based on measured feature contributions to remove unnecessary rules.

Main Results:

  • The proposed method successfully simplified various DRF models and benchmark sensor datasets.
  • Simplified DRF models demonstrated robust performance with significantly fewer parameters and rules.
  • Comparison with compressed DNNs showed superior parameter compression and memory efficiency for the simplified DRF.

Conclusions:

  • The developed rule elimination technique effectively simplifies DRF models.
  • The simplified DRF models offer improved transparency, efficiency, and comparable classification accuracy to complex models.
  • This approach provides a more interpretable and resource-efficient alternative to DNNs for complex tasks.