Cost-sensitive feature selection based on the Set Covering Machine

Raúl Santos-Rodríguez; Darío García-García

doi:10.1109/ICDMW.2010.92

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 proceeding › Conference 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 language	English
Title of host publication	Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Pages	740-746
Number of pages	7
DOIs	https://doi.org/10.1109/ICDMW.2010.92
Publication status	Published - 2010
Event	10th IEEE International Conference on Data Mining Workshops, ICDMW 2010 - Sydney, NSW, Australia Duration: 14 Dec 2010 → 17 Dec 2010

Publication series

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

Conference

Conference	10th IEEE International Conference on Data Mining Workshops, ICDMW 2010
Country	Australia
City	Sydney, NSW
Period	14/12/10 → 17/12/10

Bibliographical note

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

Keywords

Cost-sensitive learning
Feature selection
Set covering machine

Access to Document

10.1109/ICDMW.2010.92

Link to publication in Scopus

Cite this

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title = "Cost-sensitive feature selection based on the Set Covering Machine",

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.",

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Santos-Rodríguez, R & García-García, D 2010, Cost-sensitive feature selection based on the Set Covering Machine. in Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010., 5693370, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 740-746, 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010, Sydney, NSW, Australia, 14/12/10. https://doi.org/10.1109/ICDMW.2010.92

Cost-sensitive feature selection based on the Set Covering Machine. / Santos-Rodríguez, Raúl; García-García, Darío.

Proceedings - 10th IEEE International Conference on Data Mining Workshops, ICDMW 2010. 2010. p. 740-746 5693370 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

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