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Analysis of Feature Rankings for Classification


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Author: Ruiz Sánchez, Roberto
Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal
Díaz Díaz, Norberto
Department: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Date: 2005
Published in: Advances in Intelligent Data Analysis VI, Lecture Notes in Computer Science, Volume 3646, pp 362-372 (2005)
Document type: Chapter of Book
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.
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