Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Machine Learning
The fuzzy sets and systems based on AFS.structure, EI algebra and EII algebra
Fuzzy Sets and Systems
Information Sciences—Informatics and Computer Science: An International Journal
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Information Sciences: an International Journal
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Extraction of fuzzy rules from support vector machines
Fuzzy Sets and Systems
Learning fuzzy rules from fuzzy samples based on rough set technique
Information Sciences: an International Journal
Compact fuzzy association rule-based classifier
Expert Systems with Applications: An International Journal
Induction of multiple fuzzy decision trees based on rough set technique
Information Sciences: an International Journal
Axiomatic Fuzzy Set Theory and Its Applications
Axiomatic Fuzzy Set Theory and Its Applications
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
Information Sciences: an International Journal
Information Sciences: an International Journal
The fuzzy clustering analysis based on AFS theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Cascaded classification of high resolution remote sensing images using multiple contexts
Information Sciences: an International Journal
Information Sciences: an International Journal
Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
Expert Systems with Applications: An International Journal
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This paper proposes a classification method that is based on easily interpretable fuzzy rules and fully capitalizes on the two key technologies, namely pruning the outliers in the training data by SVMs (support vector machines), i.e., eliminating the influence of outliers on the learning process; finding a fuzzy set with sound linguistic interpretation to describe each class based on AFS (axiomatic fuzzy set) theory. Compared with other fuzzy rule-based methods, the proposed models are usually more compact and easily understandable for the users since each class is described by much fewer rules. The proposed method also comes with two other advantages, namely, each rule obtained from the proposed algorithm is simply a conjunction of some linguistic terms, there are no parameters that are required to be tuned. The proposed classification method is compared with the previously published fuzzy rule-based classifiers by testing them on 16 UCI data sets. The results show that the fuzzy rule-based classifier presented in this paper, offers a compact, understandable and accurate classification scheme. A balance is achieved between the interpretability and the accuracy.