Minimum Redundancy Feature Selection

Author(s)	O.Conrad (c) 2014
Library ID	table_calculus
Tool ID	12
Version	1.0
Menu	Table \| Calculus

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/

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Data	table, input	`DATA`	-	-
Output	Feature Selection	table, output	`SELECTION`	-	-
Options	Class Identifier	table field	`CLASS`	-	-
	Verbose Output	boolean	`VERBOSE`	output of intermediate results to execution message window	Default: 1
	Number of Features	integer number	`mRMR_NFEATURES`	-	Minimum: 1 Default: 50
	Discretization	boolean	`mRMR_DISCRETIZE`	uncheck this means no discretizaton (i.e. data is already integer)	Default: 1
	Discretization Threshold	floating point number	`mRMR_THRESHOLD`	a double number of the discretization threshold; set to 0 to make binarization	Minimum: 0.000000 Default: 1.000000
	Selection Method	choice	`mRMR_METHOD`	-	Available Choices: [0] Mutual Information Difference (MID) [1] Mutual Information Quotient (MIQ) Default: 0

Command Line


Usage: saga_cmd table_calculus 12 [-DATA ] [-CLASS ] [-SELECTION ] [-VERBOSE ] [-mRMR_NFEATURES ] [-mRMR_DISCRETIZE ] [-mRMR_THRESHOLD ] [-mRMR_METHOD ]
  -DATA:                Data
	table, input
  -CLASS:               Class Identifier
	table field
  -SELECTION:           Feature Selection
	table, output
  -VERBOSE:             Verbose Output
	boolean
	Default: 1
  -mRMR_NFEATURES:      Number of Features
	integer number
	Minimum: 1
	Default: 50
  -mRMR_DISCRETIZE:     Discretization
	boolean
	Default: 1
  -mRMR_THRESHOLD:   Discretization Threshold
	floating point number
	Minimum: 0.000000
	Default: 1.000000
  -mRMR_METHOD:
                             Selection Method
                             	choice
                             	Available Choices:
                             	[0] Mutual Information Difference (MID)
                             	[1] Mutual Information Quotient (MIQ)
                             	Default: 0