Heuristic Search over a Ranking for Feature Selection
In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Estado: | Versión publicada |
| Fecha de publicación: | 2005 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39715 |
| Acceso en línea: | http://hdl.handle.net/11441/39715 https://doi.org/10.1007/11494669_91 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Pattern recognition Algorithm analysis Image processing Computer vision Evolutionary biology |
| Sumario: | In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited feature set for distinguishing experiment classes in high dimensional data sets. Our method is based on the relevance and redundancy idea, in the sense that a ranked-feature is chosen if additional information is gained by adding it. This heuristic leads to considerably better accuracy results, in comparison to the full set, and other representative feature selection algorithms in twelve well–known data sets, coupled with notable dimensionality reduction. |
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