Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Extracting comprehensible models from trained neural networks
Extracting comprehensible models from trained neural networks
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
A Discretization Algorithm Based on a Heterogeneity Criterion
IEEE Transactions on Knowledge and Data Engineering
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
A representative validation of a neural 3G admission control through rules extraction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Extracting reducible knowledge from ANN with JBOS and FCANN approaches
Expert Systems with Applications: An International Journal
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Nowadays, Artificial Neural Networks (ANN) are being widely used in the representation of different systems and physics processes. Once trained, the networks are capable of dealing with operational conditions not seen during the training process, keeping tolerable errors in their responses. However, humans cannot assimilate the knowledge kept by those networks, since such implicit knowledge is difficult to be extracted. In this work, Formal Concept Analysis (FCA) is being used in order to extract and represent knowledge from previously trained ANN. The new FCANN approach permits to obtain a complete canonical base, non-redundant and with minimum implications, which qualitatively describes the process being studied. The approach proposed has a sequence of steps such as the generation of a synthetic dataset. The variation of data number per parameter and the discretization interval number are adjustment factors to obtain more representative rules without the necessity of retraining the network. The FCANN method is not a classifier itself as other methods for rule extraction; this approach can be used to describe and understand the relationship among the process parameters through implication rules. Comparisons of FCANN with C4.5 and TREPAN algorithms are made to show its features and efficacy. Applications of the FCANN method for real world problems are presented as case studies.