Abstract
This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented.
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Keywords
- Classification Accuracy
- Class Description
- Specific Generalization
- Inductive Logic Programming
- Generalization Algorithm
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© 1994 Springer-Verlag Berlin Heidelberg
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Emde, W. (1994). Inductive learning of characteristic concept descriptions from small sets of classified examples. In: Bergadano, F., De Raedt, L. (eds) Machine Learning: ECML-94. ECML 1994. Lecture Notes in Computer Science, vol 784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57868-4_53
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DOI: https://doi.org/10.1007/3-540-57868-4_53
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