Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Multilayer feedforward networks are universal approximators
Neural Networks
Unified theories of cognition
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Fuzzy evolutionary computation
Fuzzy evolutionary computation
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Soft Computing and Intelligent Systems: Theory and Applications
Soft Computing and Intelligent Systems: Theory and Applications
Fuzzy Sets, Neural Networks and Soft Computing
Fuzzy Sets, Neural Networks and Soft Computing
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Fuzzy Relation Equations and Their Applications to Knowledge Engineering
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Granular computing: an emerging paradigm
Granular computing: an emerging paradigm
The problem of linguistic approximation in system analysis
The problem of linguistic approximation in system analysis
Human Problem Solving
Toward a theory of validation of hybrid minmax FuzzyNeuro systems
MMACTEE'08 Proceedings of the 10th WSEAS International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering
The effect of class imbalance, complexity, size, and learning distribution on classifier performance
International Journal of Advanced Intelligence Paradigms
Fuzzy rule-based segmentation of CT brain images of hemorrhage for compression
International Journal of Advanced Intelligence Paradigms
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We propose to accommodate herein our novel unified granular framework that uses a developed hybrid fuzzy-neuro relational system in order to tackle a complex medical diagnosis problem and to understand the influence of syndromes in relation to symptoms. To this goal, we propose to adapt our novel computational granular unified framework that is cognitively-motivated for learning IF-THEN fuzzy weighted diagnosis rules by using a hybrid neuro-fuzzy or fuzzy-neuro possibilistic model appropriately crafted as a means to automatically extract or learn diagnosis rules from only input-output examples by integrating some useful concepts from the human cognitive processes and adding some interesting granular functionalities. This learning scheme uses an exhaustive search over the fuzzy partitions of involved variables, automatic fuzzy hypotheses generation, formulation and testing, and approximation procedure of min-max relational equations. The main idea is to start learning from coarse fuzzy partitions of the involved proteins variations input variables and proceed progressively toward fine-grained partitions until finding the appropriate partitions that fit the data. According to the complexity of the problem at hand, it learns the whole structure of the fuzzy system, i.e., conjointly appropriate fuzzy partitions, appropriate fuzzy diagnosis rules, their number and their associated trapezoidal membership functions.