Fast Feature Ranking Algorithm
The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m at...
| Autores: | , , |
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| Tipo de documento: | capítulo de livro |
| Estado: | Versão publicada |
| Data de publicação: | 2003 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39228 |
| Acesso em linha: | http://hdl.handle.net/11441/39228 https://doi.org/10.1007/978-3-540-45224-9_46 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Artificial Intelligence (incl. Robotics) Computer Communication Networks Information Storage and Retrieval Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction IT in Business |
| Resumo: | The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant attributes, allow making models of classification simpler and easy to understand. The algorithm has some interesting characteristics: lower computational cost (O(m n log n) m attributes and n examples in the data set) with respect to other typical algorithms due to the absence of distance and statistical calculations; its applicability to any labelled data set, that is to say, it can contain continuous and discrete variables, with no need for transformation. In order to test the relevance of the new feature selection algorithm, we compare the results induced by several classifiers before and after applying the feature selection algorithms. |
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