Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/30503
Title: Fuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance
Authors: Vanloffelt, Marnick
NAPOLES RUIZ, Gonzalo 
VANHOOF, Koen 
Issue Date: 2019
Publisher: IEEE
Source: Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), p. 922-928.
Abstract: Pattern classification is a popular research field within the Machine Learning discipline. Black-box models have proven to be potent classifiers in this particular field. However, their inability to provide a transparent decision mechanism is often regarded as an undesirable feature. Fuzzy-Rough Cognitive Networks are granular classifiers that have proven competitive and effective in such tasks. In this paper, we examine the contribution of the FRCN's main building blocks, being the causal weight matrix and the activation values of the neurons, to the model's average performance. Noise injection is employed to this end. Our findings suggest that optimising the weight matrix might not be as beneficial to the model's performance as suggested in previous research. Furthermore, we found that a powerful activation of the neurons included in the model topology is crucial to performance, as expected. Further research should as such focus on finding more powerful ways to activate these neurons, rather than focus on optimising the causal weight matrix.
Keywords: Index Terms-pattern classification;fuzzy-rough sets;granular classifiers;fuzzy rough cognitive networks
Document URI: http://hdl.handle.net/1942/30503
ISBN: 9781728145501
DOI: 10.1109/ICMLA.2019.00159
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
ICMLA 2019 - paper.pdf
  Restricted Access
Early view675.93 kBAdobe PDFView/Open    Request a copy
Fuzzy-Rough Cognitive Networks Building Blocks and Their Contribution to Performance.pdf
  Restricted Access
Proof of peer review54.88 kBAdobe PDFView/Open    Request a copy
Show full item record

Page view(s)

74
checked on May 24, 2022

Download(s)

2
checked on May 24, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.