Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A. Zadeh
Fuzzy prototype model and semantic distance
Information Systems
LFOIL: Linguistic rule induction in the label semantics framework
Fuzzy Sets and Systems
Fuzzy decision tree based approach to predict the type of pavement repair
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
WSEAS Transactions on Information Science and Applications
Evaluation of biometric systems: a study of users' acceptance and satisfaction
International Journal of Biometrics
Hi-index | 0.00 |
In data mining for knowledge explanation purposes, we would like to build simple transparent fuzzy models. Compared to other fuzzy models, simple fuzzy logic rules (IF ... THEN... rules) based on triangular or trapezoidal shape fuzzy sets are much simpler and easier to understand. For fuzzy rule based learning algorithms, choosing the right combination of attributes and fuzzy sets which have the most information is the key point to obtain good accuracy. On the other hand, the fuzzy ID3 algorithm gives an efficient model to select the right combinations. We therefore discover the set of simple fuzzy logic rules from a fuzzy decision tree based on the same simple shaped fuzzy partition, after dropping those rules whose credibility is less than a reasonable threshold, only if the accuracy of the training set using these rules is reasonably close to the accuracy using fuzzy decision tree. The set of simple fuzzy logic rules satisfied with this condition is also able to be used to interpret the information of the tree. Furthermore, we use the fuzzy set operator "OR" to merge simple fuzzy logic rules to reduce the number of rules.