A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.

John H. Holmes; Malek Kamoun; Ajay Israni; Marshall Joffe; Wei Yang; Harold I. Feldman

A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.

John H. Holmes, Malek Kamoun, Ajay Israni, Marshall Joffe, Wei Yang, Harold I. Feldman

Research output: Contribution to journal › Article › peer-review

Abstract

Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.

Original language	English (US)
Pages (from-to)	978
Number of pages	1
Journal	AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
State	Published - 2007
Externally published	Yes

ASJC Scopus subject areas

General Medicine

Cite this

@article{4a0f009142d846a5a7711a18f7915d79,

title = "A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.",

abstract = "Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.",

author = "Holmes, {John H.} and Malek Kamoun and Ajay Israni and Marshall Joffe and Wei Yang and Feldman, {Harold I.}",

year = "2007",

language = "English (US)",

pages = "978",

journal = "AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium",

issn = "1559-4076",

publisher = "American Medical Informatics Association",

}

TY - JOUR

T1 - A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.

AU - Holmes, John H.

AU - Kamoun, Malek

AU - Israni, Ajay

AU - Joffe, Marshall

AU - Yang, Wei

AU - Feldman, Harold I.

PY - 2007

Y1 - 2007

N2 - Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.

AB - Missing data are often imputed for analysis, but a single imputation may be inaccurate when performing feature selection in mining data. Feature selection procedures applied to multiply imputed data demonstrate this phenomenon and suggest that multiple imputation is an important adjunct to knowledge discovery.

UR - http://www.scopus.com/inward/record.url?scp=56149103075&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56149103075&partnerID=8YFLogxK

M3 - Article

C2 - 18694078

AN - SCOPUS:56149103075

SN - 1559-4076

SP - 978

JO - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

ER -

A comparison of four feature selection algorithms applied to multiply-imputed proteomic data.

Abstract

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this