KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Permanent link to this recordhttp://hdl.handle.net/10754/564245
MetadataShow full item record
AbstractThe 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.
PublisherSpringer Science + Business Media
Conference/Event name5th International Conference on Rough Set and Knowledge Technology, RSKT 2010