Abstract
This paper investigates modelling concepts as a few, large convex hulls rather than as many, small, axis-orthogonal divisions as is done by systems which currently dominate classification learning. It is argued that this approach produces classifiers which have less strong hypothesis language bias and which, because of the fewness of the concepts induced, are more understandable. The design of such a system is described and its performance is investigated.Convex hulls are shown to be a useful inductive generalisation technique offering rather different biases than well-known systems such as C4.5 and CN2. The types of domains where convex hulls can be usefully employed are described.
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Newlands, D.A., Webb, G.I. (1999). Convex Hulls in Concept Induction. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_42
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DOI: https://doi.org/10.1007/3-540-48912-6_42
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