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Decision trees can initialize radial-basis function networks.

¹Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette, LA 70504-4330, USA.

IEEE Transactions on Neural Networks

|February 8, 2008

Summary

This summary is machine-generated.

This study introduces a novel approach to Radial-Basis Function (RBF) networks using decision trees. This method enhances classification accuracy and network efficiency by addressing common implementation challenges.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Radial-Basis Function (RBF) networks face challenges in classification, including architectural complexity, irrelevant attributes, and scaling issues.
Existing RBF network implementations often struggle with efficiency and accuracy due to these inherent problems.

Purpose of the Study:

To propose a new method for initializing RBF networks that overcomes common implementation hurdles.
To improve the compactness, ease of induction, and classification accuracy of RBF networks.

Main Methods:

Initializing RBF networks using decision trees to define distinct regions in the instance space.
Assigning one basis function to each identified pure region.

Main Results:

The proposed method results in a compact RBF network architecture.
The networks are easier to induce and demonstrate favorable classification accuracy.
Irrelevant attributes and scaling issues are effectively managed through this approach.

Conclusions:

Decision tree initialization provides an effective strategy for building efficient and accurate RBF networks.
This approach offers a practical solution for overcoming key challenges in RBF network implementation for classification tasks.