Criteria for selecting a variable in the construction of efficient decision trees

Two variable selection criteria are proposed for converting a decision table to a near-optimum decision tree in the sense of minimal average cost of testing. A criterion, Q, is introduced that is based on the potential of a decision table. The previously known criterion `loss´ and Q are combined into a third criterion O. The performance of the three criteria is examined both theoretically and experimentally. Of most importance is that Q and O do not select a nonessential variable, while `loss´ may do so. It is also shown that the performance of the three criteria is not worse than that of any other known heuristics, at least for a particular example. The algorithm requires at most O(L² 2^L) operations, where L is the arity of an input table