Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Computing iceberg concept lattices with TITANIC
Data & Knowledge Engineering
Concept Data Analysis: Theory and Applications
Concept Data Analysis: Theory and Applications
Complexity Reduction in Lattice-Based Information Retrieval
Information Retrieval
Engineering Applications of Artificial Intelligence
Formal concept analysis with background knowledge: attribute priorities
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Refinement of approximate domain theories by knowledge-based neural networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Are artificial neural networks black boxes?
IEEE Transactions on Neural Networks
Applying the JBOS reduction method for relevant knowledge extraction
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Due to its ability to handle nonlinear problems, artificial neural networks are applied in several areas of science. However, the human elements are unable to assimilate the knowledge kept in those networks, since such knowledge is implicitly represented by their connections and the respective numerical weights. In recent formal concept analysis, through the FCANN method, it has demonstrated a powerful methodology for extracting knowledge from neural networks. However, depending on the settings used or the number of the neural network variables, the number of formal concepts and consequently of rules extracted from the network can make the process of knowledge and learning extraction impossible. Thus, this paper addresses the application of the JBOS approach to extracted reduced knowledge from the formal contexts extracted by FCANN from the neural network. Thus, providing a small number of formal concepts and rules for the final user, without losing the ability to understand the process learned by the network.