C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
A framework for linguistic modelling
Artificial Intelligence
Decision tree learning with fuzzy labels
Information Sciences—Informatics and Computer Science: An International Journal
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Modelling and Reasoning with Vague Concepts (Studies in Computational Intelligence)
Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem
Intelligent information processing II
Classification and query evaluation using modelling with words
Information Sciences: an International Journal
Linguistic modelling and information coarsening based on prototype theory and label semantics
International Journal of Approximate Reasoning
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
A prototype-based rule inference system incorporating linear functions
Fuzzy Sets and Systems
Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Prediction and query evaluation using linguistic decision trees
Applied Soft Computing
Hybrid Bayesian estimation tree learning with discrete and fuzzy labels
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Label semantics is a random set framework for modelling with words. In previous work, several machine learning algorithms based on this framework have been proposed and studied. In this paper, we introduce a new linguistic rule induction algorithm based on Quinlan's FOIL algorithm. According to this algorithm, a set of linguistic rules is generated for classification problems. The new model is empirically tested on an artificial toy problem and several benchmark problems from UCI repository. The results show that the new model can generate very compact linguistic rules while maintaining comparable accuracy to other well-known data mining algorithms.