Modeling and Experimentation Framework for Fuzzy Cognitive Maps
Authors: Maikel Leon Espinosa, Gonzalo Napoles Ruiz
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Case studies were conducted and are illustrated with the intention of demonstrating the success and practical value of the general approach together with the implementation tool. and Figure 3 illustrates, as an example, the learning progress for the drug IDV [3] where experts can inspect relevant statistics (e.g. problem features, accuracy, convergence, number of decision classes). They are updated step-by-step, which allow visualizing the algorithm progress in real-time. |
| Researcher Affiliation | Academia | Maikel Leon Espinosa Department of Business Technology, University of Miami, USA and Gonzalo Napoles Ruiz Faculty of Business Economics, Hasselt University, Belgium |
| Pseudocode | No | The paper describes algorithms and software architecture but does not include any structured pseudocode or algorithm blocks. It refers to other papers for detailed mathematical formulations of algorithms. |
| Open Source Code | No | The paper describes a software tool (FCM TOOL) with 20,000 lines of Java code, but there is no explicit statement about the code being open-source or publicly available, nor any link to a repository. |
| Open Datasets | No | The paper mentions 'the drug IDV [3]' as an example for learning progress, citing a paper by I. Grau Garcia et al. (2013). While a problem/dataset is referenced via citation, this paper does not explicitly state that the dataset used for its case studies is publicly available, nor does it provide a direct link or concrete access information for it. |
| Dataset Splits | No | The paper illustrates 'learning progress' with statistics like accuracy and convergence, but it does not provide specific details on dataset splitting (e.g., percentages for training, validation, or testing sets), nor does it describe cross-validation setups or other data partitioning methods. |
| Hardware Specification | No | The paper describes FCM TOOL as a 'platform-independent software tool' but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory, cloud instances) used for running its experiments or developing the tool. |
| Software Dependencies | No | The paper states the software is 'completely written in Java language' but does not specify the version of Java used or list any other software dependencies with their specific version numbers. |
| Experiment Setup | No | The paper mentions 'learning algorithms for adjusting the introduced parameters' and illustrates 'learning progress,' but it does not provide specific details such as hyperparameter values (e.g., learning rate, batch size, epochs), optimizer settings, or other concrete experimental setup configurations. |