Learning smooth dendrite morphological neurons for pattern classification using linkage trees and evolutionary-based hyperparameter tuning

Samuel Omar Tovias-Alanis

doi:10.1016/j.patrec.2023.05.024

Learning smooth dendrite morphological neurons for pattern classification using linkage trees and evolutionary-based hyperparameter tuning

2023-08-01

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AI

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Samuel Omar Tovias-Alanis

H

Humberto Sossa

W

Wilfrido Gómez‐Flores *

DOI：10.1016/j.patrec.2023.05.024

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Abstract

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The current learning approach for smooth dendrite morphological neurons (DMNs) determines dendrite parameters using k-means clustering, which is non-reproducible due to its stochastic nature, risking falling into local minima. To overcome this issue, we introduce a DMN learning approach based on a deterministic hierarchical clustering method, which builds a linkage tree for each class of patterns. In ad-dition, a micro genetic algorithm automatically tunes the cut-off points in the linkage trees hierarchy to create suitable clusters of dendrites. The classification experiments consider 40 real-world datasets. The proposed approach outperforms three DMN models in classification performance and is quite competitive with a hybrid morphological-linear perceptron, multilayer perceptron, random forest, and support vector machine. Therefore, the proposed method is a suitable alternative for pattern classification applications.& COPY; 2023 Elsevier B.V. All rights reserved.

Keywords:

Dendrite morphological neurons

Spherical dendrites

Linkage trees

Pattern classification

Genetic algorithm

Learning smooth dendrite morphological neurons for pattern classification using linkage trees and evolutionary-based hyperparameter tuning

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