PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Martelli A., Montanari U. Optimizing decision trees through heuristically guided search. In: Communication ACM, vol. 21 (12) pp. 1025 - 1039. 1978.
 
 
Abstract
(English)
Optimal decision table conversion has been tackled in the literature using two approaches, dynamic programming and branch-and-bound. The former technique is quite effective, but its time and space requirements are independent of how "easy" the given table is. Furthermore, it cannot be used to produce good, quasioptimal solutions. The branch-and-bound technique uses a good heuristic to direct the search space, since the number of solutions increases with the number of test variables according to a double exponential. In this paper we suggest a heuristically guided top-down search algorithm which, like dynamic programming, recognizes identical subproblems but which can be used to find both optimal and quasioptimal solutions. The heuristic search method introduced in this paper combines the positive aspects of the above two techniques. Compressed tables with a large number of variables can be handled without deriving expanded tables first.
Subject decision table
decision tree
heuristic search
AND/OR graphs
dynamic programming


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