Abstract
The paper describes a tool which allows us for relatively small decision tables to make consecutive optimization of decision trees relative to various complexity measures such as number of nodes, average depth, and depth, and to find parameters and the number of optimal decision trees. © 2010 Springer-Verlag Berlin Heidelberg.
Original language | English (US) |
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Title of host publication | Rough Set and Knowledge Technology |
Publisher | Springer Nature |
Pages | 353-360 |
Number of pages | 8 |
ISBN (Print) | 3642162479; 9783642162473 |
DOIs | |
State | Published - 2010 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science