Heterogeneous Forests of Decision Trees
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Rule Extraction from Self-Organizing Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Extraction of Logical Rules from Data by Means of Piecewise-Linear Neural Networks
DS '02 Proceedings of the 5th International Conference on Discovery Science
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Rule extraction: using neural networks or for neural networks?
Journal of Computer Science and Technology
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
International Journal of Data Mining and Bioinformatics
International Journal of Hybrid Intelligent Systems - VIII Brazilian Symposium On Neural Networks
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
Genetic Programming and Evolvable Machines
Measures of ruleset quality for general rules extraction methods
International Journal of Approximate Reasoning
Cognitive Architectures: Where do we go from here?
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Computer Methods and Programs in Biomedicine
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
DensityRank: a novel feature ranking method based on kernel estimation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Agents learn from human experts: an approach to test reconfigurable systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Learning highly non-separable Boolean functions using constructive feedforward neural network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Knowledge based descriptive neural networks
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Pruning classification rules with reference vector selection methods
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
On the extraction of decision support rules from fuzzy predictive models
Applied Soft Computing
Learning data structures with inherent complex logic: neurocognitive perspective
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Support vector neural training
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A novel method for extracting knowledge from neural networks with evolving SQL queries
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Expert Systems with Applications: An International Journal
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
A model for single and multiple knowledge based networks
Artificial Intelligence in Medicine
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A new methodology of extraction, optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require determination of linguistic variables or membership functions. Contest-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables. A tradeoff between accurary/simplicity is explored at the rule extraction stage and between rejection/error level at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to “soft trapezoidal” membership functions and allowing to optimize the linguistic variables using gradient procedures. Numerous applications of this methodology to benchmark and real-life problems are reported and very simple crisp logical rules for many datasets provided