Abstract
A new definition of possibility measure is presented which is calculated on truth space and is shown to be equivalent to Zadeh's original definition. This alternative formulation is shown to be the more natural in the context of decision classification because it clearly demonstrates the need for determining both the possibility of a category and not that category in a selection criterion. A number of useful possibility theorems are presented and their application to decision classification is demonstrated in a simplistic medical diagnosis problem, which also employs entropy measure as an additional parameter. The truth space formulation of possibility measure is shown to be of further value in problems of high dimensional state and an important possibility theorem relating to such problems is presented.
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