Module Minimum Redundancy Feature Selection
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/
- Author: O.Conrad (c) 2014
- Specification: grid
- Menu: Table|Calculus
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 | 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 | 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 <str> [-CLASS <str>] [-SELECTION <str>] [-VERBOSE <str>] [-mRMR_NFEATURES <num>] [-mRMR_DISCRETIZE <str>] [-mRMR_THRESHOLD <str>] [-mRMR_METHOD <str>]
-DATA:<str> Data
Table (input)
-CLASS:<str> Class Identifier
Table field
-SELECTION:<str> Feature Selection
Table (output)
-VERBOSE:<str> Verbose Output
Boolean
Default: 1
-mRMR_NFEATURES:<num> Number of Features
Integer
Minimum: 1
Default: 50
-mRMR_DISCRETIZE:<str> Discretization
Boolean
Default: 1
-mRMR_THRESHOLD:<str> Discretization Threshold
Floating point
Minimum: 0.000000
Default: 1.000000
-mRMR_METHOD:<str> Selection Method
Choice
Available Choices:
[0] Mutual Information Difference (MID)
[1] Mutual Information Quotient (MIQ)
Default: 0