About: A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification

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rdf:type	Item
description	wetenschappelijk artikel (nl) наукова стаття, опублікована у 2015 (uk) im Januar 2015 veröffentlichter wissenschaftlicher Artikel (de)
publication date	wds:Q59119490-AD84A2EA-62F1-4508-8219-02B2EA7FF128
publication date	2015-01-01 00:00:00Z (xsd:dateTime)
cites work	wds:Q59119490-01CD5FD3-B690-400C-A86E-0B8CF38775DC wds:Q59119490-2F03FBB2-304D-4C11-8798-21F851043FB4 wds:Q59119490-408BC15D-5F7A-45AE-A20B-CD548A71066F wds:Q59119490-7594278C-773B-45C7-9AA2-09375B272AC1 wds:Q59119490-892D3321-A522-4DAE-B915-6E1F74B1F359 wds:Q59119490-93703ADD-438E-4DAB-8884-5567993D546C wds:Q59119490-A2EFB821-FA11-408D-B643-21EA3BA65595 wds:Q59119490-BC072D47-C656-453A-833E-5D62F000F9DC wds:Q59119490-C1909663-F083-41B0-8FF6-BC77F58893C6
cites work	Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests Brain Reading Using Full Brain Support Vector Machines for Object Recognition: There Is No “Face” Identification Area An ensemble correlation-based gene selection algorithm for cancer classification with gene expression data MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS Cancer classification using ensemble of neural networks with multiple significant gene subsets A novel representation for apoptosis protein subcellular localization prediction using support vector machine. Geosensor data representation using layered slope grids Trigger learning and ECG parameter customization for remote cardiac clinical care information system
author name string	wds:Q59119490-01E1028A-5568-4A7C-B0CF-D9C875D1FE4E wds:Q59119490-37088B79-DBB1-4878-94D3-6074EA941EDA wds:Q59119490-3750E5BB-0247-4110-9459-A17BAEEABD02 wds:Q59119490-41A8B2D6-FB8C-44C5-9F39-25E4D358FD4D wds:Q59119490-5FACCDCE-2557-4256-9759-C14CCC9FD230 wds:Q59119490-774B70F2-F4E4-4D84-A254-B2FA9A0C7113 wds:Q59119490-8A5945E6-D5D2-4F10-BB5B-7B304A295742
author name string	Ho Sun Shon Keun Ho Ryu Yongjun Piao Minghao Piao Cheng Hao Jin Buhyun Hwang Ji-Moon Chung
rdfs:label	A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (en) A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (nl)
skos:prefLabel	A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (en) A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (nl)
name	A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (en) A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (nl)
title	wds:Q59119490-51F2371E-3E84-4FCF-B770-B348B3668DFF
title	A New Ensemble Method with Feature Space Partitioning for High-Dimensional Data Classification (en)
page(s)	wds:Q59119490-C4ECE090-8358-4BC3-8D3F-BE5E4CC470AB
page(s)	1-12
instance of	wds:Q59119490-15427740-72E0-455C-AA13-7298C6D1C9B5
instance of	scholarly article
main subject	wds:Q59119490-74C3193C-AE8D-41BB-90DC-78E2ED7AE75D
main subject	high-dimensional data
published in	wds:Q59119490-CA718E70-57FD-4DF1-81C6-8DA69BC6FB13
published in	Mathematical Problems in Engineering
volume	wds:Q59119490-8CDC0BC9-78B3-4FF8-AA35-BD68A3A86FB0
volume	2015
number of pages	wds:Q59119490-A6C54F31-E350-4C10-B1DA-A8730204FA6A
number of pages	12 (xsd:decimal)
DOI	wds:Q59119490-CEF9139E-86F6-4497-B45C-8C32D4A25797
DOI	http://dx.doi.org/10.1155/2015/590678
DOI	10.1155/2015/590678
copyright license	wds:Q59119490-AB6F7091-897E-48C0-9406-2DCA1F65860E
maintained by WikiProject	wds:Q59119490-1D7D8599-5027-474D-B011-0F6431AA58E2
copyright status	wds:Q59119490-FA95E31F-4627-49DA-81E5-BB8616DE8A6A
copyright license	Creative Commons Attribution 3.0 Unported
maintained by WikiProject	WikiProject Mathematics
copyright status	copyrighted
zbMATH Open document ID	wds:Q59119490-F56F617E-B402-49A8-B792-1A3C3D8E0190
zbMATH Open document ID	1395.62171
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q59119490

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