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

Data

format
href
Please set a value for DATA.

Class Identifier

format
href

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.

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Last modified: Sat Feb 19 15:43:34 CET 2022