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An adaptive methodology to discretize and select features

 

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Opened Access An adaptive methodology to discretize and select features
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Author: Álvarez de la Concepción, Miguel Ángel
González Abril, Luis
Soria Morillo, Luis Miguel
Ortega Ramírez, Juan Antonio
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Universidad de Sevilla. Departamento de Economía Aplicada I
Date: 2013
Published in: Artificial Intelligence Research, 2 (2), 77-86.
Document type: Article
Abstract: 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.
Size: 101.1Kb
Format: PDF

URI: http://hdl.handle.net/11441/55021

DOI: 10.5430/air.v2n2p77

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This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

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