C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Learning Logical Definitions from Relations
Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Possibilistic instance-based learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Weighting fuzzy classification rules using receiver operating characteristics (ROC) analysis
Information Sciences: an International Journal
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Intelligent Data Analysis
Introducing possibilistic logic in ILP for dealing with exceptions
Artificial Intelligence
Learning valued preference structures for solving classification problems
Fuzzy Sets and Systems
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Selection of relevant features in a fuzzy genetic learningalgorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
Fuzzy rule induction in a set covering framework
IEEE Transactions on Fuzzy Systems
Fuzzy Classification Using Pattern Discovery
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Introducing the Discriminative Paraconsistent Machine (DPM)
Information Sciences: an International Journal
A structured view on sources of uncertainty in supervised learning
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
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This paper introduces a fuzzy-rule-based classification method called fuzzy round robin [repeated incremental pruning to produce error reduction (RIPPER)] (FR3). As the name suggests, FR3 builds upon the RIPPER algorithm, a state-of-the-art rule learner.More specifically, in the context of polychotomous classification, it uses a fuzzy extension of RIPPER as a base learner within a round robin scheme, and thus, can be seen as a fuzzy variant of the R3 learner that has recently been introduced in the literature. A key feature of FR3, in comparison with its nonfuzzy counterpart, is its ability to represent different facets of uncertainty involved in a classification decision in a more faithful way. FR3 thus provides the basis for implementing "reliable classifiers" that may abstain from a decision when not being sure enough, or at least indicate that a classification is not fully supported by the empirical evidence at hand. Besides, our experimental results show that FR3 outperforms R3 in terms of classification accuracy, and therefore, suggest that it produces predictions that are not only more reliable but also more accurate. The superb classification performance of FR3 is furthermore confirmed by comparing it to other state-of-the-art (fuzzy) rule learners.