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Classification of Systems-II

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Related Experiment Video

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Published on: February 15, 2017

Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering.

Jenni Raitoharju, Serkan Kiranyaz, Moncef Gabbouj

IEEE Transactions on Neural Networks and Learning Systems

|December 2, 2015

Summary

This summary is machine-generated.

Class-specific clustering for radial basis function neural networks (RBFNNs) reduces complexity and improves classification performance. This approach optimizes Gaussian neuron placement, especially for networks with fewer neurons.

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Area of Science:

Machine Learning
Artificial Intelligence
Computational Neuroscience

Background:

Radial basis function neural networks (RBFNNs) commonly use clustering to determine Gaussian neuron locations.
Existing methods include unsupervised input clustering and supervised input-output clustering.

Purpose of the Study:

To evaluate the benefits of class-specific clustering for training RBFNNs.
To compare class-specific clustering with input and input-output clustering regarding classification performance and computational efficiency.

Main Methods:

Implemented class-specific clustering for RBFNN training.
Compared three clustering algorithms across 25 benchmark datasets.
Combined multidimensional particle swarm optimization with class-specific clustering for centroid optimization.

Main Results:

Class-specific clustering significantly reduces overall clustering complexity.
Demonstrated significant gains in classification performance, particularly for RBFNNs with fewer Gaussian neurons.
Showcased the novel combination of dynamic evolutionary optimization and class-specific clustering.

Conclusions:

Class-specific clustering is a beneficial approach for training RBFNNs.
This method enhances classification accuracy and computational efficiency.
Optimizing cluster centroids using evolutionary methods alongside class-specific clustering shows promise.