Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Senawi, Azlyna, Wei, Hua-Liang, Billings, Stephen A. (2017) A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking. Pattern Recognition, 67. 47-61 doi:10.1016/j.patcog.2017.01.026

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitleA new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking
JournalPattern Recognition
AuthorsSenawi, AzlynaAuthor
Wei, Hua-LiangAuthor
Billings, Stephen A.Author
Year2017 (July)Volume67
PublisherElsevier BV
DOIdoi:10.1016/j.patcog.2017.01.026Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID9366937Long-form Identifiermindat:1:5:9366937:7
GUID0
Full ReferenceSenawi, Azlyna, Wei, Hua-Liang, Billings, Stephen A. (2017) A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking. Pattern Recognition, 67. 47-61 doi:10.1016/j.patcog.2017.01.026
Plain TextSenawi, Azlyna, Wei, Hua-Liang, Billings, Stephen A. (2017) A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking. Pattern Recognition, 67. 47-61 doi:10.1016/j.patcog.2017.01.026
In(2017) Pattern Recognition Vol. 67. Elsevier BV

See Also

These are possibly similar items as determined by title/reference text matching only.

Zuo, Xianyu, Zhang, Wenbo, Wang, Xiangyu, Dang, Lanxue, Qiao, Baojun, Wang, Yadi (2025) Unsupervised feature selection via maximum relevance and minimum global redundancy. Pattern Recognition, 164. doi:10.1016/j.patcog.2025.111483
Liang, Jianning, Yang, Su, Winstanley, Adam (2008) Invariant optimal feature selection: A distance discriminant and feature ranking based solution. Pattern Recognition, 41 (5). 1429-1439 doi:10.1016/j.patcog.2007.10.018
Wang, Jianzhong, Wu, Lishan, Kong, Jun, Li, Yuxin, Zhang, Baoxue (2013) Maximum weight and minimum redundancy: A novel framework for feature subset selection. Pattern Recognition, 46 (6). 1616-1627 doi:10.1016/j.patcog.2012.11.025
Zhang, Daoqiang, Chen, Songcan, Zhou, Zhi-Hua (2008) Constraint Score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, 41 (5). 1440-1451 doi:10.1016/j.patcog.2007.10.009
Feng, Jie, Jiao, Licheng, Liu, Fang, Sun, Tao, Zhang, Xiangrong (2016) Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images. Pattern Recognition, 51. 295-309 doi:10.1016/j.patcog.2015.08.018
Zeng, Hong, Cheung, Yiu-Ming (2009) A new feature selection method for Gaussian mixture clustering. Pattern Recognition, 42 (2). 243-250 doi:10.1016/j.patcog.2008.05.030
Mohsenzadeh, Yalda, Sheikhzadeh, Hamid, Nazari, Sobhan (2016) Incremental relevance sample-feature machine: A fast marginal likelihood maximization approach for joint feature selection and classification. Pattern Recognition, 60. 835-848 doi:10.1016/j.patcog.2016.06.028
Solorio-Fernández, Saúl, Martínez-Trinidad, José Fco., Carrasco-Ochoa, J. Ariel (2017) A new Unsupervised Spectral Feature Selection Method for mixed data: A filter approach. Pattern Recognition, 72. 314-326 doi:10.1016/j.patcog.2017.07.020
Zhou, Peng, Hu, Xuegang, Li, Peipei, Wu, Xindong (2019) OFS-Density: A novel online streaming feature selection method. Pattern Recognition, 86. 48-61 doi:10.1016/j.patcog.2018.08.009
Lee, Jaesung, Kim, Dae-Won (2015) Fast multi-label feature selection based on information-theoretic feature ranking. Pattern Recognition, 48 (9). 2761-2771 doi:10.1016/j.patcog.2015.04.009
Zhang, Zhihong, Bai, Lu, Liang, Yuanheng, Hancock, Edwin (2017) Joint hypergraph learning and sparse regression for feature selection. Pattern Recognition, 63. 291-309 doi:10.1016/j.patcog.2016.06.009
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are © OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 17, 2025 16:18:44
Go to top of page