Selecting Most Adaptable
Diagnostic Solutions through
Pivoting-Based Retrieval

Luigi Portinale, Pietro Torasso and Diego Magro

Dipartimento di Informatica - Universita di Torino
C.so Svizzera 185 - 10149 Torino (ITALY)
e-mail: {portinal,torasso}@di.unito.it


Abstract. The aim of the present paper is to investigate a retrieval strategy
for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a
tight integration between retrieval and adaptation estimation. It exploits a
heuristic estimate of the adaptability of a solution; during retrieval, lower and
upper bounds for such an estimate are computed for relevant cases and a pivot
case is selected, determining which cases have to be considered and which
have not. Such a technique has been evaluated on three different domain
modeis and very satisfactory results have been obtained both in terms of
accuracy, space and retrieval time
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