CBFS: High Performance Feature Selection Algorithm Based on FeatureClearness

Collection with item attached
2012
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/475740.do
DOI
10.1371/journal.pone.0040419
Title
CBFS: High Performance Feature Selection Algorithm Based on FeatureClearness
Description
This study was supported by grant No. R31-2008-000-10069-0 from theWorld Class University (WCU) project of the Ministry of Education,Science & Technology (MEST) and the Korea Science and EngineeringFoundation (KOSEF). The funders had no role in study design, datacollection and analysis, decision to publish, or preparation of themanuscript.
abstract
Background: The goal of feature selection is to select useful features and simultaneously exclude garbage features from a given dataset for classification purposes. This is expected to bring reduction of processing time and improvement of classification accuracy.
Methodology: In this study, we devised a new feature selection algorithm (CBFS) based on clearness of features. Feature clearness expresses separability among classes in a feature. Highly clear features contribute towards obtaining high classification accuracy. CScore is a measure to score clearness of each feature and is based on clustered samples to centroid of classes in a feature. We also suggest combining CBFS and other algorithms to improve classification accuracy.
Conclusions/Significance: From the experiment we confirm that CBFS is more excellent than up-to-date feature selection algorithms including FeaLect. CBFS can be applied to microarray gene selection, text categorization, and image classification.
provenance
Made available in Cube on 2018-09-28T11:20:58Z (GMT). No. of bitstreams: 0
language
English
author
Seo, Minseok
Oh, Sejong
accessioned
2018-09-28T11:20:58Z
available
2018-09-28T11:20:58Z
issued
2012
citation
PLOS ONE(7): 7
issn
1932-6203
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/475740.do
Funder
교육과학기술부
Funding Program
지방대학경쟁력기반확충(지방세계수준의연구중심대학육성_일반)(0.5)
Project ID
1345196391
Jurisdiction
Rep.of Korea
Project Name
Research on Regenerative Medicine by Nano-Bio-Information-Medical Convergence Technology
rights
openAccess
type
article


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