Knowledge Discovery from fMRI Brain Images by Logical Regression Analysis
DS '00 Proceedings of the Third International Conference on Discovery Science
Rule Discovery from fMRI Brain Images by Logical Regression Analysis
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks
DS '02 Proceedings of the 5th International Conference on Discovery Science
Growing neural network trees efficiently and effectively
Design and application of hybrid intelligent systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Extracting linguistic quantitative rules from supervised neural networks
International Journal of Knowledge-based and Intelligent Engineering Systems
Computers & Mathematics with Applications
Measures of Ruleset Quality Capable to Represent Uncertain Validity
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
WSEAS Transactions on Computers
Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system
Engineering Applications of Artificial Intelligence
Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach
Fuzzy Sets and Systems
Outlier identify based on BP neural network in dam safety monitoring
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Reverse Engineering the Neural Networks for Rule Extraction in Classification Problems
Neural Processing Letters
Artificial Intelligence in Medicine
Prediction of the Amount of Wood Using Neural Networks
Journal of Mathematical Modelling and Algorithms
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Presents an algorithm for extracting rules from trained neural networks. The algorithm is a decompositional approach which can be applied to any neural network whose output function is monotone such as a sigmoid function. Therefore, the algorithm can be applied to multilayer neural networks, recurrent neural networks and so on. It does not depend on training algorithms, and its computational complexity is polynomial. The basic idea is that the units of neural networks are approximated by Boolean functions. But the computational complexity of the approximation is exponential, and so a polynomial algorithm is presented. The author has applied the algorithm to several problems to extract understandable and accurate rules. The paper shows the results for the votes data, mushroom data, and others. The algorithm is extended to the continuous domain, where extracted rules are continuous Boolean functions. Roughly speaking, the representation by continuous Boolean functions means the representation using conjunction, disjunction, direct proportion, and reverse proportion. This paper shows the results for iris data