On theory revision with queries
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
More theory revision with queries (extended abstract)
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Effective and Efficient Knowledge Base Refinement
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Scalability Issues in Inductive Logic Programming
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learning
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Data mining tasks and methods: Clustering: conceptual clustering
Handbook of data mining and knowledge discovery
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This article examines the problem of concept formation in machine learning, and focuses in particular on the problem of aggregation, i .e., the decision of which objects are to be grouped together into a new concept. While existing concept formation approaches have mainly concentrated on aggregation constraints that rely on structural or correlational properties of the concepts themselves, we argue that in an integrated learning system, other learning activities can provide an additional context that focuses concept formation before structural criteria are applied. In particular, we present the concept formation method realized by the KRT and CLT components of the integrated learning system MOBAL. In MOBAL, a concept formation attempt is triggered whenever no existing concept can adequately capture the rule instance and exception sets as they arise from the theory revision activities of the system. We describe how the so-proposed aggregate is characterized by a set of (function-free) first-order Horn clauses and how these are evaluated according to structural criteria to decide about the introduction of the concept into the representation. We show how a structural criterion can be used to ensure that any new concept improves the structure of the knowledge base, and we empirically evaluate how the introduction of new concepts according to different criteria affects the classification accuracy of learned rules.