Introducing attribute risk for retrieval in case-based reasoning

  • Authors:

  • Juan L. Castro;Maria Navarro;José M. Sánchez;José M. Zurita

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, Spain

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2011

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Abstract

One of the major assumptions in case-based reasoning is that similar experiences can guide future reasoning, problem solving and learning. This assumption shows the importance of the method used for choosing the most suitable case, especially when dealing with the class of problems in which risk, is relevant concept to the case retrieval process. This paper argues that traditional similarity assessment methods are not sufficient to obtain the best case; an additional step with new information must be performed necessary, after applying similarity measures in the retrieval stage. When a case is recovered from the case base, one must take into account not only the specific value of the attribute but also whether the case solution is suitable for solving the problem, depending on the risk produced in the final decision. We introduce this risk, as new information through a new concept called risk information that is entirely different from the weight of the attributes. Our article presents this concept locally and measures it for each attribute independently.