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dc.contributor.authorAzad, Mohammad
dc.contributor.authorMoshkov, Mikhail
dc.date.accessioned2015-08-04T07:27:38Z
dc.date.available2015-08-04T07:27:38Z
dc.date.issued2014-09-29
dc.identifier.isbn9788360810583
dc.identifier.doi10.15439/2014F256
dc.identifier.urihttp://hdl.handle.net/10754/564997
dc.description.abstractWe used decision tree as a model to discover the knowledge from multi-label decision tables where each row has a set of decisions attached to it and our goal is to find out one arbitrary decision from the set of decisions attached to a row. The size of the decision tree can be small as well as very large. We study here different greedy as well as dynamic programming algorithms to minimize the size of the decision trees. When we compare the optimal result from dynamic programming algorithm, we found some greedy algorithms produce results which are close to the optimal result for the minimization of number of nodes (at most 18.92% difference), number of nonterminal nodes (at most 20.76% difference), and number of terminal nodes (at most 18.71% difference).
dc.publisherPolish Information Processing Society PTI
dc.titleMinimizing size of decision trees for multi-label decision tables
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
dc.identifier.journalProceedings of the 2014 Federated Conference on Computer Science and Information Systems
dc.conference.date7 September 2014 through 10 September 2014
dc.conference.name2014 Federated Conference on Computer Science and Information Systems, FedCSIS 2014
kaust.personAzad, Mohammad
kaust.personMoshkov, Mikhail


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