Título | On learning similarity relations in fuzzy case-based reasoning |

Publication Type | Conference Paper |

Year of Publication | 2004 |

Authors | Armengol E [1], Esteva F [2], Godo L [3], Torra V [4] |

Editor | Peters J.F. [5], Skowron A. [6], Dubois D [7], Grzymala-Busse J. [8], Inuiguchi M. [9], Polkowski L [10] |

Conference Name | Lecture Notes in Computer Science |

Volume | 3135 |

Editorial | Springer |

Paginación | 14-32 |

Resumen | Case-based reasoning (CBR) is a problem solving technique that puts at work the general principle that similar problems have similar solutions. In particular, it has been proved effective for classification problems. Fuzzy set-based approaches to CBR relyon the existence of a fuzzy similitary functions on the problem description and problem solution domains. In this paper, we study the problem of learning a global similarity measure in the problem description domain as a weighted average of the atribute-based similarities and, therefore, the learning problem consists in finding the weighting vector that minimizes mis-classification. The approach is validated by comparing results with an application of case-based reasoning in a medical domain that uses a diferent model. |