liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On the Complexity of Discrete Feature Selection for Optimal Classification
Linköping University, Department of Computer and Information Science, IISLAB - Laboratory for Intelligent Information Systems. Linköping University, The Institute of Technology. (ADIT)
Harvard University.
2010 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 32, no 8, 1517-U1522 p.Article in journal (Refereed) Published
Abstract [en]

Consider a classification problem involving only discrete features that are represented as random variables with some prescribed discrete sample space. In this paper, we study the complexity of two feature selection problems. The first problem consists in finding a feature subset of a given size k that has minimal Bayes risk. We show that for any increasing ordering of the Bayes risks of the feature subsets (consistent with an obvious monotonicity constraint), there exists a probability distribution that exhibits that ordering. This implies that solving the first problem requires an exhaustive search over the feature subsets of size k. The second problem consists of finding the minimal feature subset that has minimal Bayes risk. In the light of the complexity of the first problem, one may think that solving the second problem requires an exhaustive search over all of the feature subsets. We show that, under mild assumptions, this is not true. We also study the practical implications of our solutions to the second problem.

Place, publisher, year, edition, pages
IEEE Institute of Electrical and Electronics , 2010. Vol. 32, no 8, 1517-U1522 p.
Keyword [en]
Feature evaluation and selection; classifier design and evaluation; machine learning
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-58348DOI: 10.1109/TPAMI.2010.84ISI: 000278858600012OAI: oai:DiVA.org:liu-58348DiVA: diva2:343352
Note
©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Jose M Pena and Roland Nilsson, On the Complexity of Discrete Feature Selection for Optimal Classification, 2010, IEEE Transaction on Pattern Analysis and Machine Intelligence, (32), 8, 1517-U1522. http://dx.doi.org/10.1109/TPAMI.2010.84 Available from: 2010-08-13 Created: 2010-08-11 Last updated: 2017-12-12

Open Access in DiVA

fulltext(281 kB)229 downloads
File information
File name FULLTEXT01.pdfFile size 281 kBChecksum SHA-512
1cee7bed21b8d60f601e9e17f9a07166f36a4e6d0936accc4538d5100d865b9cc6a1b8900454302fb71135812e10adf977e450f3a76c16367e0d83eadd014cf9
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Pena, Jose M

Search in DiVA

By author/editor
Pena, Jose M
By organisation
IISLAB - Laboratory for Intelligent Information SystemsThe Institute of Technology
In the same journal
IEEE Transaction on Pattern Analysis and Machine Intelligence
Engineering and Technology

Search outside of DiVA

GoogleGoogle Scholar
Total: 229 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 92 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf