Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Fuzzy learning models in expert systems
Fuzzy Sets and Systems - Special Double issue Fuzzy Set Theory in the USSR
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 4
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Machine learning: an artificial intelligence approach volume III
Machine learning: an artificial intelligence approach volume III
Artificial intelligence (3rd ed.)
Artificial intelligence (3rd ed.)
Fuzzy logic with linguistic quantifiers in inductive learning
Fuzzy logic for the management of uncertainty
C4.5: programs for machine learning
C4.5: programs for machine learning
An inductive learning procedure to identify fuzzy systems
Fuzzy Sets and Systems
Learning rules for a fuzzy inference model
Fuzzy Sets and Systems - Special issue on fuzzy data analysis
Machine Learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Machine Learning
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In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from “soft” instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms.