Attribute Clustering Using Rough Set Theory For Feature Selection In Fault Severity Classification Of Rotating Machinery

Research output: Contribution to journalArticleResearchpeer-review

37 Citations (Scopus)
Original languageEnglish (US)
JournalExpert Systems with Applications
Volume71
Issue number7400377
DOIs
StatePublished - 26 Nov 2016

Cite this

@article{ff2d6cd695154333b02d0aa8b08f60fa,
title = "Attribute Clustering Using Rough Set Theory For Feature Selection In Fault Severity Classification Of Rotating Machinery",
author = "{Sanchez Loja}, {Rene Vinicio} and {Cabrera Mendieta}, {Diego Roman}",
year = "2016",
month = "11",
day = "26",
doi = "10.1016/J.ESWA.2016.11.024",
language = "English (US)",
volume = "71",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "7400377",

}

TY - JOUR

T1 - Attribute Clustering Using Rough Set Theory For Feature Selection In Fault Severity Classification Of Rotating Machinery

AU - Sanchez Loja, Rene Vinicio

AU - Cabrera Mendieta, Diego Roman

PY - 2016/11/26

Y1 - 2016/11/26

U2 - 10.1016/J.ESWA.2016.11.024

DO - 10.1016/J.ESWA.2016.11.024

M3 - Article

VL - 71

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 7400377

ER -