A New Maximum Relevance-Minimum Multicollinearity (MRmMC) Method for Feature Selection and Ranking

Senawi, A., Wei, H. orcid.org/0000-0002-4704-7346 and Billings, S.A. (2017) A New Maximum Relevance-Minimum Multicollinearity (MRmMC) Method for Feature Selection and Ranking. Pattern Recognition, 67. pp. 47-61. ISSN 0031-3203

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2017 Elsevier Ltd. This is an author produced version of a paper subsequently published in Pattern Recognition. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Keywords: Dimensionality reduction; feature selection; classification; correlation measure; qualitative and quantitative variables
Dates:
  • Accepted: 18 January 2017
  • Published (online): 1 February 2017
  • Published: July 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
FunderGrant number
EUROPEAN COMMISSION - HORIZON 2020PROGRESS - 637302
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/I011056/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/H00453X/1
Depositing User: Symplectic Sheffield
Date Deposited: 01 Feb 2017 11:09
Last Modified: 01 Feb 2018 01:38
Published Version: http://dx.doi.org/10.1016/j.patcog.2017.01.026
Status: Published
Publisher: Elsevier
Refereed: Yes
Identification Number: https://doi.org/10.1016/j.patcog.2017.01.026

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