Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/32439
Title: | Pattern classification with Evolving Long-term Cognitive Networks | Authors: | NAPOLES RUIZ, Gonzalo Jastrzebska, Agnieszka Salgueiro, Yamisleydi |
Issue Date: | 2021 | Publisher: | ELSEVIER SCIENCE INC | Source: | Information sciences (Print), 548, p. 461-478 | Abstract: | This paper presents an interpretable neural system-termed Evolving Long-term Cognitive Network-for pattern classification. The proposed model was inspired by Fuzzy Cognitive Maps, which are interpretable recurrent neural networks for modeling and simulation. The network architecture is comprised of two neural blocks: a recurrent input layer and an output layer. The input layer is a Long-term Cognitive Network that gets unfolded in the same way as other recurrent neural networks, thus producing a sort of abstract hidden layers. In our model, we can attach meaningful linguistic labels to each neuron since the input neurons correspond to features in a given classification problem and the output neurons correspond to class labels. Moreover, we propose a variant of the backpropagation learning algorithm that can be applied to compute the required parameters. This algorithm includes two new regularization components that are aimed at obtaining more interpretable knowledge representations. The numerical simulations using 58 datasets show that our model achieves higher prediction rates when compared with traditional white boxes while remaining competitive with the black boxes. Finally, we elaborate on the interpretability of our neural system using a proof of concept. | Keywords: | Long-term Cognitive Networks;Recurrent Neural Networks;Backpropagation;Interpretability | Document URI: | http://hdl.handle.net/1942/32439 | ISSN: | 0020-0255 | e-ISSN: | 1872-6291 | DOI: | 10.1016/j.ins.2020.08.058 | ISI #: | WOS:000596037700010 | Rights: | 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S0020025520308240-main.pdf | Published version | 1.37 MB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
4
checked on Oct 12, 2024
Page view(s)
26
checked on Jun 3, 2022
Download(s)
2
checked on Jun 3, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.