Cost-sensitive feature selection based on the Set Covering Machine

Raúl Santos-Rodríguez*, Darío García-García

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)

Abstract

This paper describes how to make use of the cost information related to the extraction of each feature in a feature selection algorithm. For instance, in medical diagnosis, the different tests a patient might take during the diagnosis process can have different associated costs. The main idea is to change the feature selection framework in order to get low-cost subsets of informative features. This work proposes a way to introduce this information in a well-known machine learning algorithm, the Set Covering Machine.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages740-746
Number of pages7
DOIs
Publication statusPublished - 2010
Event10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia
Duration: 14 Dec 201017 Dec 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
CountryAustralia
CitySydney, NSW
Period14/12/1017/12/10

Bibliographical note

Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

Keywords

  • Cost-sensitive learning
  • Feature selection
  • Set covering machine

Cite this