Decision rules, trees and tests for tables with many-valued decisions–comparative study
Procedia Computer Science, 2013•Elsevier
In this paper, we present three approaches for construction of decision rules for decision
tables with many-valued decisions. We construct decision rules directly for rows of decision
table, based on paths in decision tree, and based on attributes contained in a test (super-
reduct). Experimental results for the data sets taken from UCI Machine Learning Repository,
contain comparison of the maximum and the average length of rules for the mentioned
approaches.
tables with many-valued decisions. We construct decision rules directly for rows of decision
table, based on paths in decision tree, and based on attributes contained in a test (super-
reduct). Experimental results for the data sets taken from UCI Machine Learning Repository,
contain comparison of the maximum and the average length of rules for the mentioned
approaches.
Abstract
In this paper, we present three approaches for construction of decision rules for decision tables with many-valued decisions. We construct decision rules directly for rows of decision table, based on paths in decision tree, and based on attributes contained in a test (super-reduct). Experimental results for the data sets taken from UCI Machine Learning Repository, contain comparison of the maximum and the average length of rules for the mentioned approaches.
Elsevier
以上显示的是最相近的搜索结果。 查看全部搜索结果