Robust regression for developing software estimation models
Journal of Systems and Software
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
Fuzzy modelling through logic optimization
International Journal of Approximate Reasoning
Predictive accuracy comparison of fuzzy models for software development effort of small programs
Journal of Systems and Software
Segmented software cost estimation models based on fuzzy clustering
Journal of Systems and Software
Fuzzy Systems Engineering: Toward Human-Centric Computing
Fuzzy Systems Engineering: Toward Human-Centric Computing
Visual comparison of software cost estimation models by regression error characteristic analysis
Journal of Systems and Software
Adaptive ridge regression system for software cost estimating on multi-collinear datasets
Journal of Systems and Software
Analogy-based software effort estimation using Fuzzy numbers
Journal of Systems and Software
Fast meta-models for local fusion of multiple predictive models
Applied Soft Computing
Fuzzy Emotional COCOMO II Software Cost Estimation (FECSCE) using Multi-Agent Systems
Applied Soft Computing
A systematic literature review of software quality cost research
Journal of Systems and Software
The optimization of success probability for software projects using genetic algorithms
Journal of Systems and Software
Credit risk evaluation using neural networks: Emotional versus conventional models
Applied Soft Computing
Adaptive dynamic RBF neural controller design for a class of nonlinear systems
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
Genetic interval neural networks for granular data regression
Information Sciences: an International Journal
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In this study, we introduce and discuss a concept of knowledge transfer in system modeling. In a nutshell, knowledge transfer is about forming ways on how a source of knowledge (namely, an existing model) can be used in presence of new, very limited experimental evidence. In virtue of the nature of the problem at hand (a situation encountered quite commonly, e.g. in project cost estimation), new data could be very limited and this scarcity of data makes it insufficient to construct a new model. At the same time, the new data originate from a similar (but not the same) phenomenon (process) for which the original model has been constructed so the existing model, even though it could applied, has to be treated with a certain level of reservation. Such situations can be encountered, e.g. in software engineering where in spite existing similarities, each project, process, or product exhibits its own unique characteristics. Taking this into consideration, the existing model is generalized (abstracted) by forming its granular counterpart - granular model where its parameters are regarded as information granules rather than numeric entities, viz. their non-numeric (granular) version is formed based on the values of the numeric parameters present in the original model. The results produced by the granular model are also granular and in this manner they become reflective of the differences existing between the current phenomenon and the process for which the previous model has been formed. In the study on knowledge transfer and reusability, information granularity is viewed as an important design asset and as such it is subject to optimization. We formulate an optimal information granularity allocation problem: assuming a certain level of granularity, distribute it optimally among the parameters of the model (making them granular) so that a certain data coverage criterion is maximized. While the underlying concept is general and applicable to a variety of models, in this study, we discuss its use to fuzzy neural networks with intent to clearly visualize the advantages of the approach and emphasize various ways of forming granular versions of the weights (parameters) of the connections of the network. Several granularity allocation protocols (ranging from a uniform distribution of granularity, symmetric and asymmetric schemes of allocation) are discussed and the effectiveness of each of them is quantified. The use of Particle Swarm Optimization (PSO) as the underlying optimization tool to realize optimal granularity allocation is discussed.