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A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm
IEEE Transactions on Neural Networks
|January 1, 1992
Summary
This study introduces a continuous ID3 algorithm to convert decision trees into neural network hidden layers, enabling self-generating feedforward network architectures and interpretable knowledge extraction. Performance was validated on spiral data.
Area of Science:
- Machine Learning
- Artificial Intelligence
- Neural Networks
Background:
- Decision trees and neural networks are distinct machine learning models.
- Interpreting the knowledge within neural network hidden layers is challenging.
Purpose of the Study:
- To describe the relationship between decision trees and neural network hidden layers.
- To propose a novel algorithm for converting decision trees into neural network hidden layers.
- To enable self-generation of feedforward neural network architectures and knowledge interpretation.
Main Methods:
- A continuous ID3 algorithm was developed to transform decision trees into hidden layers.
- Feedforward neural network architectures were self-generated.
- Knowledge embedded in connections and weights was interpreted.
- Cauchy training (fast simulated annealing) was used to avoid local minima.
Main Results:
- The continuous ID3 algorithm successfully converted decision trees into neural network hidden layers.
- Self-generation of feedforward neural network architectures was achieved.
- The algorithm facilitated interpretation of embedded knowledge.
- Performance analysis on spiral data demonstrated the algorithm's efficacy.
Conclusions:
- The proposed continuous ID3 algorithm bridges decision trees and neural networks.
- This approach allows for interpretable and self-generating neural network models.
- The method shows promise for enhancing understanding and application of neural networks.
