Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Information retrieval
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Automated Refinement of First-Order Horn-Clause Domain Theories
Machine Learning
Representing problem-solving for knowledge refinement
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Designing scripts to guide users in modifying knowledge-based systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Selective sampling for nearest neighbor classifiers
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Knowledge Refinement for a Design System
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Organising Knowledge Refinement Operators
EUROVAV '99 Collected papers from the 5th European Symposium on Validation and Verification of Knowledge Based Systems - Theory, Tools and Practice
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Statistics for Business and Economics (with Student CD-ROM, iPod Key Term, and InfoTrac )
Statistics for Business and Economics (with Student CD-ROM, iPod Key Term, and InfoTrac )
Active learning with statistical models
Journal of Artificial Intelligence Research
An OO Model for Incremental Hierarchical KA
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
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Knowledge refinement tools rely on a representative set of training examples to identify and repair faults in a knowledge based system (KBS). In real environments it is often difficult to obtain a large set of examples since each problem-solving task must be labelled with the expert's solution. However, it is often somewhat easier to generate unlabelled tasks that cover the expertise of a KBS. This paper investigates ways to select a suitable sample from a set of unlabelled problem-solving tasks, so that only the subset requires to be labelled. The unlabelled examples are clustered according to the way they are solved by the KBS and selection is targeted on these clusters. Experiments in two domains showed that selective sampling reduced the number of training examples used for refinement, and hence requiring to be labelled. Moreover, this reduction was possible without affecting the accuracy of the final refined KBS. A single example selected randomly from each cluster was effective in one domain, but the other required a more informed selection that takes account of potentially conflicting repairs.