SAGA.table_calculus.12: Minimum Redundancy Feature Selection

Identify the most relevant features for subsequent classification of tabular data.<br/><br/>The minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm has been developed by Hanchuan Peng <hanchuan.peng@gmail.com>.<br/><br/>References:<br/>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.<br/><br/>Minimum redundancy feature selection from microarray gene expression data,<br/>Chris Ding, and Hanchuan Peng, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 2, pp.185-205, 2005.<br/><br/>Hanchuan Peng's mRMR Homepage at <a target="_blank" href="http://penglab.janelia.org/proj/mRMR/">http://penglab.janelia.org/proj/mRMR/</a><br/>

Inputs

Donnée

format
href
Please set a value for DATA.

Class Identifier

format
href
Please set a value for CLASS.

output of intermediate results to execution message window

boolean

Number of Features

integer

uncheck this means no discretizaton (i.e. data is already integer)

boolean

a double number of the discretization threshold; set to 0 to make binarization

number

Selection Method

string

Outputs

Feature Selection

format
transmission

Execution options

successUri
inProgressUri
failedUri

format

mode

Execute End Point

View the execution endpoint of a process.

View the alternative version in HTML.

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http://demo.mapmint.com/ogc-apihttp://localhost/ogc-api/processes/SAGA.table_calculus.12.html
Last modified: Sat Feb 19 15:43:34 CET 2022