Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions
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Main article
Type
Conference PaperAuthors
Azad, Mohammad
Moshkov, Mikhail
KAUST Department
Applied Mathematics and Computational Science ProgramComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2014-09-13Online Publication Date
2014-09-13Print Publication Date
2014Permanent link to this record
http://hdl.handle.net/10754/550702Metadata
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The paper is devoted to the analysis of greedy algorithms for the minimization of average depth of decision trees for decision tables such that each row is labeled with a set of decisions. The goal is to find one decision from the set of decisions. When we compare with the optimal result obtained from dynamic programming algorithm, we found some greedy algorithms produces results which are close to the optimal result for the minimization of average depth of decision trees.Citation
Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions 2014, 35:368 Procedia Computer SciencePublisher
Elsevier BVJournal
Procedia Computer ScienceConference/Event name
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S1877050914010825Scopus Count
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Decision and Inhibitory Rule Optimization for Decision Tables with Many-valued Decisions(2016-04-25) [Dissertation]
Advisor: Moshkov, Mikhail
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Optimization of decision rule complexity for decision tables with many-valued decisions(2013 IEEE International Conference on Systems, Man, and Cybernetics, Institute of Electrical and Electronics Engineers (IEEE), 2013-10) [Conference Paper]We describe new heuristics to construct decision rules for decision tables with many-valued decisions from the point of view of length and coverage which are enough good. We use statistical test to find leaders among the heuristics. After that, we compare our results with optimal result obtained by dynamic programming algorithms. The average percentage of relative difference between length (coverage) of constructed and optimal rules is at most 6.89% (15.89%, respectively) for leaders which seems to be a promising result. © 2013 IEEE.
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Decision rules for decision tables with many-valued decisions(Rough Sets and Knowledge Technology, Springer Nature, 2011) [Conference Paper]In the paper, authors presents a greedy algorithm for construction of exact and partial decision rules for decision tables with many-valued decisions. Exact decision rules can be 'over-fitted', so instead of exact decision rules with many attributes, it is more appropriate to work with partial decision rules with smaller number of attributes. Based on results for set cover problem authors study bounds on accuracy of greedy algorithm for exact and partial decision rule construction, and complexity of the problem of minimization of decision rule length. © 2011 Springer-Verlag.
