Test case generation by means of learning techniques
SIGSOFT '93 Proceedings of the 1st ACM SIGSOFT symposium on Foundations of software engineering
Testing by means of inductive program learning
ACM Transactions on Software Engineering and Methodology (TOSEM)
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
An Adjustable Description Quality Measure for Pattern Discovery Usingthe AQ Methodology
Journal of Intelligent Information Systems - Special issue on methodologies for intelligent information systems
Data-Driven Constructive Induction
IEEE Intelligent Systems
IEEE Transactions on Knowledge and Data Engineering
A Comparison of Rule Matching Methods Used in AQ15 and LERS
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Recognizing and Discovering Complex Events in Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Learning Patterns in Noisy Data: The AQ Approach
Machine Learning and Its Applications, Advanced Lectures
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Classifying Uncovered Examples by Rule Stretching
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology
Intelligent Data Analysis
Using case-based reasoning in interpreting unsupervised inductive learning results
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
RBDT-1: A New Rule-Based Decision Tree Generation Technique
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
Building Knowledge Scouts Using KGL Metalanguage
Fundamenta Informaticae
Classification of Unseen Examples under Uncertainty
Fundamenta Informaticae
On Learning Decision Structures
Fundamenta Informaticae
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This paper describes a method for learning flexible concepts, by which are meant concepts that lack precise definition and are context-dependent. To describe such concepts, the method employs a two-tiered representation, in which the first tier captures explicitly basic concept properties, and the second tier characterizes allowable concept's modifications and context dependency. In the proposed method, the first tier, called Base Concept Representation (BCR), is created in two phases. In phase 1, the AQ-15 rule learning program is applied to induce a complete and consistent concept description from supplied examples. In phase 2, this description is optimized according to a domain-dependent quality criterion. The second tier, called the inferential concept interpretation (ICI), consists of a procedure for flexible matching, and a set of inference rules. The proposed method has been implemented in the POSEIDON system, and experimentally tested on two real-world problems: learning the concept of an acceptable union contract, and learning voting patterns of Republicans and Democrats in the U.S. Congress. For comparison, a few other learning methods were also applied to the same problems. These methods included simple variants of exemplar-based learning, and an ID-3-type decision tree learning, implemented in the ASSISTANT program. In the experiments, POSEIDON generated concept descriptions that were both, more accurate and also substantially simpler than those produced by the other methods.