Minimum Redundancy Feature Selection
- Author: O.Conrad (c) 2014
- 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
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