Minimization of decision tree average depth for decision tables with many-valued decisions
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.
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.
Abstract
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.
Elsevier
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