Performance Analysis of Granular versus Traditional Neural Network Classifiers: Preliminary Results

Felix, Gerardo; Npoles, Gonzalo; Falcón, Rafael; Bello, Rafael; Vanhoof, Koen

doi:10.1109/civemsa.2018.8439971

Cited by 4 publications

(2 citation statements)

References 32 publications

(27 reference statements)

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“…Actually, we would only need to verify that the new algorithms retain the prediction power of the FRCN classifier. The reader is referred to [7], [11], [12], [10] and [23] for further detail on the FRCNs' prediction performance on structured classification problems.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Concepción

Nápoles

Grau

et al. 2022

IEEE Trans. Cybern.

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Section: Numerical Simulationsmentioning

confidence: 99%

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Concepción

Nápoles

Grau

et al. 2022

IEEE Trans. Cybern.

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show abstract

“…In [30] the prediction power of the FRCN algorithm was in-depth studied against shallow neural network classifiers, finding mainly on one hand how the topology attached to the FRCN network does not scale up with the number of attributes, but with the number of decision classes. On the other hand, the fact that the weight matrix can be defined beforehand (based on the semantics of fuzzy-rough granules) shows the advantages of having meaningful information constructs.…”

Section: Literature Reviewmentioning

confidence: 99%

Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

Nápoles¹,

Salgueiro²,

Grau³

et al. 2021

Preprint

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Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their meaningful and safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to specific predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose an LTCNbased model for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by unique fixed points while introducing a deterministic learning method to compute the learnable parameters. The simulations show that our interpretable model obtains competitive performance when compared to the stateof-the-art white and black boxes.

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Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

Nápoles

Salgueiro

Grau

et al. 2023

IEEE Trans. Cybern.

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published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

Performance Analysis of Granular versus Traditional Neural Network Classifiers: Preliminary Results

Cited by 4 publications

References 32 publications

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification

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