Optimization of decision rule complexity for decision tables with many-valued decisions
Type
Conference Paper
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionApplied Mathematics and Computational Science Program
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
Date
2013-10Metadata
Show full item recordAbstract
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.Conference/Event name
2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013ISBN
9780769551548Handle URI
http://hdl.handle.net/10754/564808Scopus Count
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Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions(Springer Nature, 2016-10-20)The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.
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Minimization of Decision Tree Average Depth for Decision Tables with Many-valued Decisions(Elsevier BV, 2014-09-13)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.
