Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/34695
Title: | Long-term Cognitive Network-based architecture for multi-label classification | Authors: | NAPOLES RUIZ, Gonzalo BELLO GARCIA, Marilyn Salgueiro, Yamisleydi |
Issue Date: | 2021 | Publisher: | PERGAMON-ELSEVIER SCIENCE LTD | Source: | Neural networks (Print), 140 , p. 39 -48 | Abstract: | This paper presents a neural system to deal with multi-label classification problems that might involve sparse features. The architecture of this model involves three sequential blocks with well-defined functions. The first block consists of a multilayered feed-forward structure that extracts hidden features, thus reducing the problem dimensionality. This block is useful when dealing with sparse problems. The second block consists of a Long-term Cognitive Network-based model that operates on features extracted by the first block. The activation rule of this recurrent neural network is modified to prevent the vanishing of the input signal during the recurrent inference process. The modified activation rule combines the neurons' state in the previous abstract layer (iteration) with the initial state. Moreover, we add a bias component to shift the transfer functions as needed to obtain good approximations. Finally, the third block consists of an output layer that adapts the second block's outputs to the label space. We propose a backpropagation learning algorithm that uses a squared hinge loss function to maximize the margins between labels to train this network. The results show that our model outperforms the state-of-the-art algorithms in most datasets. | Keywords: | Long-term cognitive networks;Recurrent neural networks;Backpropagation;Multi-label classification | Document URI: | http://hdl.handle.net/1942/34695 | ISSN: | 0893-6080 | e-ISSN: | 1879-2782 | DOI: | 10.1016/j.neunet.2021.03.001 | ISI #: | WOS:000652749900004 | Rights: | 2021 The Author(s). Published by Elsevier Ltd. 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-S0893608021000812-main.pdf | Published version | 881.68 kB | Adobe PDF | View/Open |
WEB OF SCIENCETM
Citations
3
checked on Oct 14, 2024
Page view(s)
30
checked on Sep 7, 2022
Download(s)
16
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.