Assessing confidence in cased based reuse step
Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving process, since the solution for a new problem is based in the available solu...
| Autores: | , , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/2679 |
| Acceso en línea: | https://hdl.handle.net/2117/2679 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Intel·ligència artificial Classificació INSPEC::Cybernetics::Artificial intelligence Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving process, since the solution for a new problem is based in the available solutions of the retrieved cases. In classification tasks a trivial reuse method is commonly used, which takes into account the most frequently solution proposed by the set of retrieved cases. We propose an alternative reuse process; we call confidence-reuse method, which make a qualitative assessment of the information retrieved. This approach is focused on measuring the solution accuracy, applying some confidence predictors based in a k-NN classifier with the aim of analyzing and evaluating the information offered by the retrieved cases. |
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