Combining case-based reasoning and statistical method for proposing solution in RICAD

J. Daengdej, D. Lukose, Yue Hong Eric Tsui, P. Beinat, L. Prophet

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Most case-based reasoning (CBR) systems concentrate on retrieving cases which are most similar to a case at hand. When a similar case is found, the system will proceed to adapt (or modify) this solution to solve the case at hand. This method of problem solving cannot be easily applied in our real-world problem domain (i.e. insurance). In this domain, sufficient number of similar cases have to be retrieved so that the system could confidently calculate the final solution. More than one similar case must be retrieved due to the fact that most of the cases which are similar to the one at hand almost always contain inconsistent results. This paper describes a CBR system called risk cost adviser (RICAD) which applies a statistical function in order to propose a reliable answer. RICAD differs from other CBR systems as, in most cases, in addition to the use of the statistical function, it has to repeat its reasoning process until an adequate number of cases are collected to calculate the answer.
Original languageEnglish
Pages (from-to)153-159
Number of pages7
JournalKnowledge-Based Systems
Volume10
Issue number3
DOIs
Publication statusPublished - 1 Oct 1997
Externally publishedYes

Keywords

  • Case-based reasoning
  • Central limit theorem

ASJC Scopus subject areas

  • Software
  • Management Information Systems
  • Information Systems and Management
  • Artificial Intelligence

Cite this