|Title||Remembering Similitude Terms in CBR|
|Publication Type||Journal Article|
|Year of Publication||2003|
|Authors||Armengol E, Plaza E|
|Journal||Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)|
In concept learning, inductive techniques perform a global approximation to the target concept. Instead, lazy learning techniques use local approximations to form an implicit global approximation of the target concept. In thisd paper we present C-LID, a lazy learning technique that uses LID for generating local approximations to the target concept. LID generates local approximations in the form of similitude terms (symbolic descriptions of what is shared by 2 or more cases). C-LID caches and reuses the similitude terms generated in past cases to improve the problem solving of future problems. The outcome of C-LID (and LID) is assessed whit experiments on the Toxicology dataset.
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