Rough Cognitive Networks (RCNs) are a kind of granular neural network that augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the rough information granules are built. In this paper, we cast the RCN model within the framework of fuzzy rough sets in an attempt to eliminate the need for a user-specified similarity threshold while retaining the model's discriminatory power. As far as we know, this is the first study that brings fuzzy sets into the domain of rough cognitive mapping. Numerical results in the presence of 140 well-known pattern classification problems reveal that our approach, referred to as Fuzzy-Rough Cognitive Networks, is capable of outperforming most traditional classifiers used for benchmarking purposes. Furthermore, we explore the impact of using different heterogeneous distance functions and fuzzy operators over the performance of our granular neural network.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.neunet.2017.08.007 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Cerebral Microbleeds and Amyloid Pathology Estimates From the Amyloid Biomarker Study.
JAMA Netw Open
January 2025
Alzheimer Center Limburg, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
Importance: Baseline cerebral microbleeds (CMBs) and APOE ε4 allele copy number are important risk factors for amyloid-related imaging abnormalities in patients with Alzheimer disease (AD) receiving therapies to lower amyloid-β plaque levels.
Objective: To provide prevalence estimates of any, no more than 4, or fewer than 2 CMBs in association with amyloid status, APOE ε4 copy number, and age.
Design, Setting, And Participants: This cross-sectional study used data included in the Amyloid Biomarker Study data pooling initiative (January 1, 2012, to the present [data collection is ongoing]).
Depressive Symptoms and Amyloid Pathology.
JAMA Psychiatry
January 2025
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
Importance: Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology.
View Article and Find Full Text PDFA double dissociation between semantic and spatial cognition in visual to default network pathways.
Elife
January 2025
Department of Psychology, University of York, North Yorkshire, United Kingdom.
Processing pathways between sensory and default mode network (DMN) regions support recognition, navigation, and memory but their organisation is not well understood. We show that functional subdivisions of visual cortex and DMN sit at opposing ends of parallel streams of information processing that support visually mediated semantic and spatial cognition, providing convergent evidence from univariate and multivariate task responses, intrinsic functional and structural connectivity. Participants learned virtual environments consisting of buildings populated with objects, drawn from either a single semantic category or multiple categories.
View Article and Find Full Text PDFMulti-study fMRI outlooks on subcortical BOLD responses in the stop-signal paradigm.
Elife
January 2025
Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands.
This study investigates the functional network underlying response inhibition in the human brain, particularly the role of the basal ganglia in successful action cancellation. Functional magnetic resonance imaging (fMRI) approaches have frequently used the stop-signal task to examine this network. We merge five such datasets, using a novel aggregatory method allowing the unification of raw fMRI data across sites.
View Article and Find Full Text PDFCo-methylation networks associated with cognition and structural brain development during adolescence.
Front Genet
January 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, United States.
Introduction: Typical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance.
Methods: To understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!