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Title: Fuzzy-Rough Cognitive Networks: Building Blocks and Their Contribution to Performance
Authors: Vanloffelt, Marnick
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
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ISBN: 9781728145501
DOI: 10.1109/ICMLA.2019.00159
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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