Index Driven Selective Sampling for CBR

In real environments it is often difficult to obtain a collection of cases for the case base that would cover all the problem solving situations. Although it is often somewhat easier to generate potential problem cases that cover the domain tasks, acquiring the solutions for the problems captured by the cases may demand valuable time of a busy expert. This paper investigates how a Case-Based Reasoning system can be empowered to actively select a small number of useful cases from a pool of problem cases, for which the expert can then provide the solution. Past cases that are complete, containing both the problem and solution, together with partial cases containing just the problem, are clustered by exploiting a decision tree index built over the complete cases. We introduce a Cluster Utility Score £¥¤§¦©¨ and Case Utility Score £©¦¨ , which then guide case selection from these clusters. Experimental results for six public domain datasets show that selective sampling techn...

• Type: Conference
• Year: 2003
• Where: ICCBR
• Authors: Nirmalie Wiratunga, Susan Craw, Stewart Massie
• Keywords: Case Base, ICCBR 2003, Partial Cases, Problem Cases