featureSelectionClassificationMRMRComponent

Pipeline component for performing MRMR feature selection in classification workflow

Since R2026a

Description

featureSelectionClassificationMRMRComponent is a pipeline component that performs feature selection using minimum redundancy maximum relevance (MRMR). The pipeline component uses the functionality of the fscmrmr function during the learn phase to identify the important predictors in the data. During the run phase, the component selects the same predictors from a new data set.

Creation

Syntax

component = featureSelectionClassificationMRMRComponent

component = featureSelectionClassificationMRMRComponent(Name=Value)

Description

component = featureSelectionClassificationMRMRComponent creates a pipeline component for feature selection using the minimum redundancy maximum relevance (MRMR) algorithm. Use the component when creating a pipeline for classification. If you want to perform MRMR feature selection as part of a regression workflow, use featureSelectionRegressionMRMRComponent instead.

component = featureSelectionClassificationMRMRComponent(Name=Value) sets writable Properties using one or more name-value arguments. For example, NumFeatures=10 specifies to select 10 important features.

Properties

Structural Parameters

The software sets structural parameters when you create the component. You cannot modify structural parameters after creating the component.

UseWeights — Observation weights flag

false or 0 (default) | true or 1

This property is read-only after the component is created.

Observation weights flag, specified as 0 (false) or 1 (true). If UseWeights is true, the component adds a third input "W" to the Inputs component property, and a third input tag 3 to the InputTags component property.

Example: c = featureSelectionClassificationMRMRComponent(UseWeights=1)

Data Types: logical

Learn Parameters

The software sets learn parameters when you create the component. You can modify learn parameters using dot notation any time before you use the learn object function. Any unset learn parameters use the corresponding default values.

NumFeatures — Number of features to select

positive integer scalar

Number of features (predictors) to select, specified as a positive integer scalar.

If you do not specify the NumFeatures or RelativeScoreThreshold value, the software selects all features. You cannot specify both NumFeatures and RelativeScoreThreshold.

Example: c = featureSelectionClassificationMRMRComponent(NumFeatures=5)

Example: c.NumFeatures = 10

Data Types: single | double

RelativeScoreThreshold — Relative score threshold for selecting features

numeric scalar in the range (0,1]

Relative score threshold for selecting features, specified as a numeric scalar in the range (0,1]. If a feature (predictor) has a score greater than or equal to the RelativeScoreThreshold value, then the software selects the feature.

If you do not specify the NumFeatures or RelativeScoreThreshold value, the software selects all features. You cannot specify both NumFeatures and RelativeScoreThreshold.

Example: c = featureSelectionClassificationMRMRComponent(RelativeScoreThreshold=0.05)

Example: c.RelativeScoreThreshold = 0.1

Data Types: single | double

Prior — Prior probabilities

"empirical" (default) | "uniform" | vector of scalar values | structure

Prior probabilities for each class, specified as a value in this table.

Value	Description
"empirical"	The class prior probabilities are the class relative frequencies in the second input argument of learn used by the component.
"uniform"	All class prior probabilities are equal to 1/K, where K is the number of classes.
numeric vector	A vector with one scalar value for each class. Each element is a class prior probability. The component normalizes the elements such that they sum to 1.
structure array	A structure S with two fields: S.ClassNames contains the class names as a variable of the same type as the second input argument of learn used by the component. S.ClassProbs contains a vector of corresponding prior probabilities. The component normalizes the elements such that they sum to 1.

If you set UseWeights to true, the component renormalizes the weights to add up to the value of the prior probability in the respective class.

Example: c = featureSelectionClassificationMRMRComponent(Prior="uniform")

Example: c.Prior = "empirical"

Data Types: single | double | char | string | struct

UseMissing — Indicator to use missing values in features

false (default) | true

Indicator to use missing values in the features (predictors), specified as either true to use the values for feature ranking and selection, or false to discard the values. For more information, see UseMissing.

Example: c = featureSelectionClassificationMRMRComponent(UseMissing=true)

Example: c.UseMissing = false

Data Types: logical

Component Properties

The software sets component properties when you create the component. You can modify the component properties (excluding HasLearnables and HasLearned) using dot notation at any time. You cannot modify the HasLearnables and HasLearned properties directly.

Name — Component identifier

"FeatureSelectionClassificationMRMR" (default) | character vector | string scalar

Component identifier, specified as a character vector or string scalar.

Example: c = featureSelectionClassificationMRMRComponent(Name="FeatureSelector")

Example: c.Name = "MRMRSelector"

Data Types: char | string

Inputs — Names of input ports

["X","Y"] (default) | character vector | string array | cell array of character vectors

Names of the input ports, specified as a character vector, string array, or cell array of character vectors. If UseWeights is true, the component adds the input port "W" to Inputs.

Example: c = featureSelectionClassificationMRMRComponent(Inputs=["Data1","Data2"])

Example: c.Inputs = ["X1","Y1"]

Data Types: char | string | cell

Outputs — Names of output ports

["XSelected","Scores"] (default) | character vector | string array | cell array of character vectors

Names of the output ports, specified as a character vector, string array, or cell array of character vectors.

Example: c = featureSelectionClassificationMRMRComponent(Outputs=["newX","importance"])

Example: c.Outputs = ["X","S"]

Data Types: char | string | cell

InputTags — Tags that enable automatic connection of component inputs

[1 2] (default) | nonnegative integer vector

Tags that enable the automatic connection of the component inputs with other components or pipelines, specified as a nonnegative integer vector. If you specify InputTags, the number of tags must match the number of inputs in Inputs. If UseWeights is true, the component adds a third input tag to InputTags.

Example: c = featureSelectionClassificationMRMRComponent(InputTags=[1 0])

Example: c.InputTags = [1 2]

Data Types: single | double

OutputTags — Tags that enable automatic connection of component outputs

[1 NaN] (default) | nonnegative integer vector

Tags that enable the automatic connection of the component outputs with other components or pipelines, specified as a nonnegative integer vector. If you specify OutputTags, the number of tags must match the number of outputs in Outputs.

Example: c = featureSelectionClassificationMRMRComponent(OutputTags=[1 0])

Example: c.OutputTags = [1 NaN]

Data Types: single | double

HasLearnables — Indicator for learnables

Read-only: 1 (true) (default)

This property is read-only.

Indicator for the learnables, returned as 1 (true). A value of 1 indicates that the component contains Learnables.

Data Types: logical

HasLearned — Indicator showing learning status of component

Read-only: 0 (false) (default) | 1 (true)

This property is read-only.

Indicator showing the learning status of the component, returned as 0 (false) or 1 (true). A value of 1 indicates that the learn object function has been applied to the component, and the Learnables are nonempty.

Data Types: logical

Learnables

The software sets learnables when you use the learn object function. You cannot modify learnables directly.

SelectedVariables — Names of features selected by component

Read-only: string array | []

This property is read-only.

Names of the features selected by the component, returned as a string array. The features correspond to columns in the first data argument of learn.

Data Types: string

UsedVariables — Names of variables used by component

Read-only: string array | []

This property is read-only.

Names of the variables used by the component to select features, returned as a string array. The variables correspond to columns in the first data argument of learn.

Data Types: string

Object Functions

learn	Initialize and evaluate pipeline or component
run	Execute pipeline or component for inference after learning
reset	Reset pipeline or component
series	Connect components in series to create pipeline
parallel	Connect components or pipelines in parallel to create pipeline
view	View diagram of pipeline inputs, outputs, components, and connections

Examples

Create Component for MRMR Feature Selection

Create a pipeline component that performs feature selection using MRMR in a classification workflow. Specify to select 3 features.

component = featureSelectionClassificationMRMRComponent(NumFeatures=3)

component = 

  featureSelectionClassificationMRMRComponent with properties:

                 Name: "FeatureSelectionClassificationMRMR"
               Inputs: ["X"    "Y"]
            InputTags: [1 2]
              Outputs: ["XSelected"    "Scores"]
           OutputTags: [1 NaN]

   
Learnables (HasLearned = false)
    SelectedVariables: []
        UsedVariables: []

   
Structural Parameters (locked)
           UseWeights: 0

   
Learn Parameters (unlocked)
          NumFeatures: 3


Show all parameters

component is a featureSelectionClassificationMRMRComponent object that contains two learnables: SelectedVariables and UsedVariables. The properties remain empty until you pass data to the component during the learn phase.

Read the fisheriris data set into a table. Store the predictor and response data in the tables X and Y, respectively.

fisheriris = readtable("fisheriris.csv");
X = fisheriris(:,1:end-1);
Y = fisheriris(:,end);

Use the learn object function to select features from the predictor data X.

component = learn(component,X,Y)

component = 

  featureSelectionClassificationMRMRComponent with properties:

                 Name: "FeatureSelectionClassificationMRMR"
               Inputs: ["X"    "Y"]
            InputTags: [1 2]
              Outputs: ["XSelected"    "Scores"]
           OutputTags: [1 NaN]

   
Learnables (HasLearned = true)
    SelectedVariables: ["PetalWidth"    "SepalWidth"    "PetalLength"]
        UsedVariables: ["SepalLength"    "SepalWidth"    "PetalLength"    "PetalWidth"]

   
Structural Parameters (locked)
           UseWeights: 0

   
Learn Parameters (locked)
          NumFeatures: 3


Show all parameters

The SelectedVariables and UsedVariables properties are nonempty, and the HasLearned property is set to true.

Find the names of the selected features.

names = component.SelectedVariables

names = 

  1×3 string array

    "PetalWidth"    "SepalWidth"    "PetalLength"

Version History

Introduced in R2026a