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Logistic model tree extraction from artificial neural networks.

Darren Dancey1, Zuhair A Bandar, David McLean

  • 1Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK. d.dancey@mmu.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|August 19, 2007
PubMed
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This study introduces a new algorithm to extract interpretable logistic model trees (LMTs) from artificial neural networks (ANNs). The new method enhances decision tree accuracy and fidelity compared to existing techniques like C4.5.

Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Artificial neural networks (ANNs) excel at pattern recognition but lack transparency, functioning as "black boxes."
  • Extracting symbolic knowledge from ANNs is crucial for understanding their decision-making processes.

Purpose of the Study:

  • To present a novel algorithm for extracting logistic model trees (LMTs) from ANNs.
  • To provide a symbolic representation of the knowledge embedded within ANNs.
  • To evaluate the performance of the new extraction algorithm against established methods.

Main Methods:

  • Developed a new algorithm to extract Landwehr's logistic model trees (LMTs) from artificial neural networks (ANNs).
  • Utilized standard decision trees with logistic regression functions in terminal nodes.

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  • Conducted an empirical evaluation using 12 benchmark datasets from the UCI machine-learning repository.

    • Main Results:

    • The new LMT extraction algorithm demonstrated higher accuracy compared to Quinlan's C4.5 and ExTree.
    • The algorithm achieved higher fidelity in representing the ANN's knowledge than C4.5 and ExTree.
    • Decision trees generated by the new method showed superior performance on benchmark datasets.

    Conclusions:

    • The proposed algorithm effectively translates complex ANN models into interpretable LMTs.
    • This approach offers a valuable method for understanding and explaining ANN decisions.
    • The extracted LMTs provide a more accurate and faithful representation of ANN knowledge.