On the conversion of decision tables to computer programs
Communications of the ACM
Use of decision tables in computer programming
Communications of the ACM
Conversion of limited-entry decision tables to computer programs
Communications of the ACM
Conversion of decision tables to computer programs by rule mask techniques
Communications of the ACM
Criteria for Selecting a Variable in the Construction of Efficient Decision Trees
IEEE Transactions on Computers
IEEE Transactions on Computers
Conversion of decision tables to efficient sequential testing procedures
Communications of the ACM
Optimal conversion of extended-entry decision tables with general cost criteria
Communications of the ACM
The synthetic approach to decision table conversion
Communications of the ACM
Communications of the ACM
Combining decision rules in a decision table
Communications of the ACM
Sequential Decision Models for Expert System Optimization
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 48.32 |
Two new algorithms for deriving optimal and near-optimal flowcharts from limited entry decision tables are presented. Both take into account rule frequencies and the time needed to test conditions. One of the algorithms, called the optimum-finding algorithm, leads to a flowchart which truly minimizes execution time for a decision table in which simple rules are already contracted to complex rules. The other one, called the optimum-approaching algorithm, requires many fewer calculations but does not necessarily produce the optimum flowchart. The algorithms are first derived for treating decision tables not containing an ELSE-rule, but the optimum-approaching algorithm is shown to be equally valid for tables including such a rule.Both algorithms are compared with existing ones and are applied to a somewhat large decision table derived from a real case. From this comparison two conclusions are drawn. (1) The optimum-approaching algorithm will usually lead to better results than comparable existing ones and will not require more, but usually less, computation time. (2) In general, the greater computation effort needed for applying the optimum-finding algorithm will not be justified by the small reduction in execution time obtained.