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Analysis of Feature Rankings for Classification


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dc.creator Ruiz Sánchez, Roberto es
dc.creator Aguilar Ruiz, Jesús Salvador es
dc.creator Riquelme Santos, José Cristóbal es
dc.creator Díaz Díaz, Norberto es 2016-04-07T11:17:52Z 2016-04-07T11:17:52Z 2005
dc.description.abstract Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these kinds of algorithms is to reduce the data set to each first attributes without losing prediction against the original data set. In this paper we propose a method, feature–ranking performance, to compare different feature–ranking methods, based on the Area Under Feature Ranking Classification Performance Curve (AURC). Conclusions and trends taken from this paper propose support for the performance of learning tasks, where some ranking algorithms studied here operate. es
dc.format application/pdf es
dc.language.iso eng es
dc.relation.ispartof Advances in Intelligent Data Analysis VI, Lecture Notes in Computer Science, Volume 3646, pp 362-372 (2005) es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri *
dc.subject Artificial intelligence es
dc.subject Information storage and retrieval es
dc.subject Probability and statistics in Computer Science es
dc.subject Pattern recognition es
dc.title Analysis of Feature Rankings for Classification es
dc.type info:eu-repo/semantics/bookPart es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.identifier.doi es
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