Loss and gain functions for CBR retrieval

  • Authors:

  • J. L. Castro;M. Navarro;J. M. Sánchez;J. M. Zurita

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, C/ Daniel Saucedo Aranda s/n, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, C/ Daniel Saucedo Aranda s/n, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, C/ Daniel Saucedo Aranda s/n, 18071 Granada, Spain;Department of Computer Science and Artificial Intelligence, ETSI Informática, Granada University, C/ Daniel Saucedo Aranda s/n, 18071 Granada, Spain

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

The method described in this article evaluates case similarity in the retrieval stage of case-based reasoning (CBR). It thus plays a key role in deciding which case to select, and therefore, in deciding which solution will be eventually applied. In CBR, there are many retrieval techniques. One feature shared by most is that case retrieval is based on attribute similarity and importance. However, there are other crucial factors that should be considered, such as the possible consequences of a given solution, in other words its potential loss and gain. As their name clearly implies, these concepts are defined as functions measuring loss and gain when a given retrieval case solution is applied. Moreover, these functions help the user to choose the best solution so that when a mistake is made the resulting loss is minimal. In this way, the highest benefit is always obtained.