Conversion of limited-entry decision tables into optimal decision trees

1971 ◽  
Vol 6 (8) ◽  
pp. 68-74 ◽  
Author(s):  
Jonas Rabin
Keyword(s):  
Decision Trees ◽  
Optimal Decision ◽  
Limited Entry ◽  
Entry Decision ◽  
Decision Tables

Correcting the description of control algorithms by limited entry decision tables

Cybernetics ◽  
1983 ◽  
Vol 19 (1) ◽  
pp. 22-28
Author(s):  
M. Ya. Kulikov ◽  
V. D. Chervenchuk
Keyword(s):  
Control Algorithms ◽  
Limited Entry ◽  
Entry Decision ◽  
Decision Tables

Ambiguity in limited entry decision tables

1968 ◽  
Vol 11 (10) ◽  
pp. 680-684 ◽  
Author(s):  
P. J. H. King
Keyword(s):  
Limited Entry ◽  
Entry Decision ◽  
Decision Tables

scholarly journals Decision Rules Derived from Optimal Decision Trees with Hypotheses

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1641
Author(s):  
Mohammad Azad ◽  
Igor Chikalov ◽  
Shahid Hussain ◽  
Mikhail Moshkov ◽  
Beata Zielosko
Keyword(s):  
Decision Trees ◽  
Decision Rules ◽  
Computer Experiments ◽  
Optimal Decision ◽  
Equivalence Queries ◽  
Minimum Number ◽  
Minimum Depth ◽  
Decision Tables ◽  
Programming Algorithms ◽  
Better Than

Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees.

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