On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Computing with words in intelligent database querying: standalone and internet-based application
Information Sciences—Informatics and Computer Science: An International Journal - Special issue computing with words
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Approximate Reasoning
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Fuzzy modelling through logic optimization
International Journal of Approximate Reasoning
Eliciting transparent fuzzy model using differential evolution
Applied Soft Computing
Fuzzy consensus measure on verbal opinions
Expert Systems with Applications: An International Journal
Collaborative clustering with the use of Fuzzy C-Means and its quantification
Fuzzy Sets and Systems
Information Sciences: an International Journal
Predictive Ensemble Pruning by Expectation Propagation
IEEE Transactions on Knowledge and Data Engineering
Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Fuzzy Sets and Systems
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
A general class of simple majority decision rules based on linguistic opinions
Information Sciences: an International Journal
Computing with words for text processing: An approach to the text categorization
Information Sciences: an International Journal
Modeling the concept of majority opinion in group decision making
Information Sciences: an International Journal
IEEE Transactions on Neural Networks
IEEE Transactions on Fuzzy Systems
Fuzzy clustering with viewpoints
IEEE Transactions on Fuzzy Systems
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bagging and Boosting Negatively Correlated Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
IEEE Transactions on Fuzzy Systems
Hierarchical aggregation functions generated from belief structures
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Aggregation Using the Linguistic Weighted Average and Interval Type-2 Fuzzy Sets
IEEE Transactions on Fuzzy Systems
Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
International Journal of Approximate Reasoning
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In system modeling, knowledge management comes vividly into the picture when dealing with a collection of individual models. These models being considered as sources of knowledge, are engaged in some collective pursuits of a collaborative development to establish modeling outcomes of global character. The result comes in the form of a so-called granular fuzzy model, which directly reflects upon and quantifies the diversity of the available sources of knowledge (local models) involved in knowledge management. In this study, several detailed algorithmic schemes are presented along with related computational aspects associated with Granular Computing. It is also shown how the construction of information granules completed through the use of the principle of justifiable granularity becomes advantageous in the realization of granular fuzzy models and a quantification of the quality (specificity) of the results of modeling. We focus on the design of granular fuzzy models considering that the locally available models are those fuzzy rule-based. It is shown that the model quantified in terms of two conflicting criteria, that is (a) a coverage criterion expressing to which extent the resulting information granules ''cover'' include data and (b) specificity criterion articulating how detailed (specific) the obtained information granules are. The overall quality of the granular model is also assessed by determining an area under curve (AUC) where the curve is formed in the coverage-specificity coordinates. Numeric results are discussed with intent of displaying the most essential features of the proposed methodology and algorithmic developments.