Instance-Based Learning Algorithms
Machine Learning
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Induction of fuzzy decision trees
Fuzzy Sets and Systems
A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Machine Learning
Learning from Inconsistent and Noisy Data: The AQ18 Approach
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Fuzzy rule extraction from ID3-type decision trees for real data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulation of fuzzy random variables
Information Sciences: an International Journal
A linear regression model for imprecise response
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Decision-tree induction and rule-learning methods have proved efficient for concept-learning and data-mining tasks. Modifications to successful algorithms learned from crisp data enable them to deal with cognitive uncertainties that, in general, use entropy as the measurement to select relevant characteristics for the learning task. A proposed heuristic approach induces rules from fuzzy databases, supported by an extension to the fuzzy case of a classical case metric, called the impurity level. An illustrative example tests the algorithm with some data sets and compares it with similar systems.