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.
| Original language | English |
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| Title of host publication | Methodologies for Knowledge Discovery and Data Mining - 3rd Pacific-Asia Conference, PAKDD 1999, Proceedings |
| Editors | Lizhu Zhou, Ning Zhong |
| Publisher | Springer |
| Pages | 306-316 |
| Number of pages | 11 |
| ISBN (Print) | 3540658661, 9783540658665 |
| Publication status | Published - 1 Jan 1999 |
| Externally published | Yes |
| Event | Pacific-Asia Conference on Knowledge Discovery and Data Mining 1999 - Beijing, China Duration: 26 Apr 1999 → 28 Apr 1999 Conference number: 3rd https://link.springer.com/book/10.1007/3-540-48912-6 (Proceedings) |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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| Volume | 1574 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | Pacific-Asia Conference on Knowledge Discovery and Data Mining 1999 |
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| Abbreviated title | PAKDD 1999 |
| Country/Territory | China |
| City | Beijing |
| Period | 26/04/99 → 28/04/99 |
| Internet address |
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Keywords
- Classification learning
- Convex hulls
- Induction