table_calculus_12-Minimum_Redundancy_Feature_Selection

Minimum Redundancy Feature Selection

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contributed at 2018-11-06

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Geography SubjectGIScience & Remote SensingSAGATableCalculus

Detailed Description

Identify the most relevant features for subsequent classification of tabular data.The minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm has been developed by Hanchuan Peng .References:Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. Hanchuan Peng, Fuhui Long, and Chris Ding, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005.Minimum redundancy feature selection from microarray gene expression data,Chris Ding, and Hanchuan Peng, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.Hanchuan Peng's mRMR Homepage at http://penglab.janelia.org/proj/mRMR/

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SAGA (2018). table_calculus_12-Minimum_Redundancy_Feature_Selection, Model Item, OpenGMS, https://geomodeling.njnu.edu.cn/modelItem/9e3ef2a9-d602-40a4-b8a5-8951f19394c3
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contributed at 2018-11-06

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