Analysis of Feature Rankings for Classification
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 k...
| Autores: | , , , |
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| Tipo de documento: | capítulo de livro |
| Estado: | Versão publicada |
| Data de publicação: | 2005 |
| País: | España |
| Recursos: | Universidad de Sevilla (US) |
| Repositório: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/39727 |
| Acesso em linha: | http://hdl.handle.net/11441/39727 https://doi.org/10.1007/11552253_33 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Artificial intelligence Information storage and retrieval Probability and statistics in Computer Science Pattern recognition |
| Resumo: | 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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