Abstract
The number of decision nodes of a diagnostic decision tree is shown to be reduced by selectively pursuing a path through the decision tree directed by the experience of prior decision outcomes. Two algorithms that provide this selectivity among paths are discussed as applications of sequential pattern recognition theory. One algorithm seeks to minimize the expected decision loss associated with each step in a multistage decision process, while the other algorithm seeks to minimize the uncertainty of a decision at each step. A comparison of the algorithms reveals that the latter provides greater selectivity among decision paths and a corresponding greater reduction in the number of decision nodes.
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