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http://hdl.handle.net/1942/27925
Title: | Performance Analysis of Granular versus Traditional Neural Network Classifiers: Preliminary Results | Authors: | Gerardo, Félix-Benjamín NAPOLES RUIZ, Gonzalo Falcon, Rafael Bello, Rafael VANHOOF, Koen |
Issue Date: | 2018 | Publisher: | IEEE | Source: | 2018 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), IEEE, | Abstract: | A recent trend in Machine Learning is to augment the transparency of traditional classification models using Granular Computing techniques. This approach has been found particularly useful in the neural networks field since most successful neural systems often require complex structures to behave like universal approximators. However, there is a widely-held view stating that, to build an interpretable classifier, one might have to sacrifice some prediction accuracy. We want to challenge this belief by exploring the performance of a recently introduced granular classifier termed Fuzzy-Rough Cognitive Networks against low-level (i.e., traditional) neural networks. The simulation results have shown that this neural system can attain quite competitive prediction rates while featuring a shallow, granular architecture. As a bigger picture, this study paves the way for conducting a more thorough evaluation of granular versus low-level neural classifiers in the near future. | Keywords: | Neurons; Computational modeling; Biological neural networks; Fuzzy sets; Market research; Analytical models; Fuzzy-Rough Cognitive Networks; Granular Computing; Neural Systems; Pattern Classification | Document URI: | http://hdl.handle.net/1942/27925 | Link to publication/dataset: | https://ieeexplore.ieee.org/document/8439971 | ISBN: | 9781538646182 | ISSN: | 2377-9322 | DOI: | 10.1109/CIVEMSA.2018.8439971 | ISI #: | 000450350700014 | Rights: | Peer-reviewed author version: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | Category: | C1 | Type: | Proceedings Paper | Validations: | ecoom 2019 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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manuscript.pdf | Peer-reviewed author version | 385.26 kB | Adobe PDF | View/Open |
10.1109@CIVEMSA.2018.8439971.pdf Restricted Access | Published version | 1.65 MB | Adobe PDF | View/Open Request a copy |
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