Evolving Recurrent Neural Networks for Pattern Classification

Gonzalo Nápoles*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


While reaching outstanding prediction rates by means of black-box classifiers is relatively easy nowadays, reaching a proper trade-off between accuracy and interpretability might become a challenge. The most popular approaches reported in the literature to overcome this problem use post-hoc procedures to explain what the classifiers have learned. Less research is devoted to building classification models able to intrinsically explain their decision process. This paper presents a recurrent neural network---termed Evolving Long-term Cognitive Network---for pattern classification, which can be deemed interpretable to some extent. Moreover, a backpropagation learning algorithm to adjust the parameters attached to the model is presented. Numerical simulations using 35 datasets show that the proposed network performs well when compared with traditional black-box classifiers.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer International Publishing
Number of pages11
ISBN (Print)9783030551803
Publication statusPublished - 2021


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