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
A substantial amount of datasets stored for various applications are often high dimensional with redundant and irrelevant features. Processing and analysing data under such circumstances is time consuming and makes it difficult to obtain efficient predictive models. There is a strong need to carry out analyses for high dimensional data in some lower dimensions, and one approach to achieve this is through feature selection. This paper presents a new relevancy-redundancy approach, called the maximum relevance–minimum multicollinearity (MRmMC) method, for feature selection and ranking, which can overcome some shortcomings of existing criteria. In the proposed method, relevant features are measured by correlation characteristics based on conditional variance while redundancy elimination is achieved according to multiple correlation assessment using an orthogonal projection scheme. A series of experiments were conducted on eight datasets from the UCI Machine Learning Repository and results show that the proposed method performed reasonably well for feature subset selection.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 PROGRESS - 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: | 10.1016/j.patcog.2017.01.026 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:111190 |
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