Automatic error localisation for categorical, continuous and integer data
Data collected by statistical offices generally contain errors, which have to be corrected before reliable data can be published. This correction process is referred to as statistical data editing. At statistical offices, certain rules, so-called edits, are often used during the editing process to d...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2005 |
| 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:2099/3757 |
| Acceso en línea: | https://hdl.handle.net/2099/3757 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical logic Statistics Artificial intelligence Mathematical programming Lògica matemàtica Estadística Intel·ligència artificial Programació (Matemàtica) Classificació AMS::03 Mathematical logic and foundations::03B General logic Classificació AMS::62 Statistics Classificació AMS::68 Computer science::68T Artificial intelligence Classificació AMS::90 Operations research, mathematical programming::90C Mathematical programming |
| Sumario: | Data collected by statistical offices generally contain errors, which have to be corrected before reliable data can be published. This correction process is referred to as statistical data editing. At statistical offices, certain rules, so-called edits, are often used during the editing process to determine whether a record is consistent or not. Inconsistent records are considered to contain errors, while consistent records are considered error-free. In this article we focus on automatic error localisation based on the Fellegi-Holt paradigm, which says that the data should be made to satisfy all edits by changing the fewest possible number of fields. Adoption of this paradigm leads to a mathematical optimisation problem. We propose an algorithm for solving this optimisation problem for a mix of categorical, continuous and integer-valued data. We also propose a heuristic procedure based on the exact algorithm. For five realistic data sets involving only integer-valued variables we evaluate the performance of this heuristic procedure. |
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