Capítulo de Libro
Fast Feature Ranking Algorithm
Autor/es | Ruiz Sánchez, Roberto
Riquelme Santos, José Cristóbal Aguilar Ruiz, Jesús Salvador |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2003 |
Fecha de depósito | 2016-03-31 |
Publicado en |
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Resumen | 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 ... 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. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Fast feature.pdf | 130.8Kb | [PDF] | Ver/ | |