Artículo
An adaptive methodology to discretize and select features
Autor/es | Álvarez de la Concepción, Miguel Ángel
González Abril, Luis Soria Morillo, Luis Miguel Ortega Ramírez, Juan Antonio |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos Universidad de Sevilla. Departamento de Economía Aplicada I |
Fecha de publicación | 2013 |
Fecha de depósito | 2017-03-01 |
Publicado en |
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Resumen | A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data
define some aspects, called features, that can be clustered in several classes. A qualitative or ... A lot of significant data describing the behavior or/and actions of systems can be collected in several domains. These data define some aspects, called features, that can be clustered in several classes. A qualitative or quantitative value for each feature is stored from measurements or observations. In this paper, the problem of finding independent features for getting the best accuracy on classification problems is considered. Obtaining these features is the main objective of this work, where an automatic method to select features is proposed. The method extends the functionality of Ameva coefficient to use it in other tasks of machine learning where it has not been defined. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | ARTEMISA TIN2009-14378-C02-01
Simon TIC-8052 |
Cita | Álvarez de la Concepción, M.Á., González Abril, L., Soria Morillo, L.M. y Ortega Ramírez, J.A. (2013). An adaptive methodology to discretize and select features. Artificial Intelligence Research, 2 (2), 77-86. |
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