featureSelectionClassificationReliefFComponent

Pipeline component for performing feature selection using ReliefF algorithm

Since R2026a

Description

featureSelectionClassificationReliefFComponent is a pipeline component that performs feature selection using the ReliefF algorithm. The pipeline component uses the functionality of the relieff function during the learn phase to identify important predictors in the data. During the run phase, the component selects the same predictors from a new data set.

Creation

Syntax

component = featureSelectionClassificationReliefFComponent(numNeighbors)

component = featureSelectionClassificationReliefFComponent(numNeighbors,Name=Value)

Description

component = featureSelectionClassificationReliefFComponent(numNeighbors) creates a pipeline component for feature selection using the ReliefF algorithm with numNeighbors nearest neighbors. Use the component when creating a pipeline for classification.

component = featureSelectionClassificationReliefFComponent(numNeighbors,Name=Value) sets writable Properties using one or more name-value arguments. For example, you can specify the method for computing weights, distance scaling factor, and prior probabilities for each class.

Input Arguments

numNeighbors — Number of nearest neighbors

positive integer scalar

Number of nearest neighbors, specified as a positive integer scalar.

Data Types: single | double

Properties

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.

Categoricalx — Categorical predictors flag

"off" (default) | "on"

Categorical predictors flag, specified as "on" or "off". If you specify "on", the component treats all predictors as categorical. Otherwise, it treats all predictors as numeric.

Example: c = featureSelectionClassificationReliefFComponent(10, Categoricalx="on")

Example: c.Categoricalx = "on"

Data Types: char | string

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 RelativeScoreThresold.

Example: c = featureSelectionClassificationReliefFComponent(10, NumFeatures=5)

Example: c.NumFeatures = 10

Data Types: single | double

NumNeighbors — Number of nearest neighbors

positive integer scalar

Number of nearest neighbors, specified as a positive integer scalar. By default, the component uses the value specified by numNeighbors.

Example: c = featureSelectionClassificationReliefFComponent(10)

Example: c.NumNeighbors = 5

Data Types: single | double

Prior — Prior probabilities for each class

"empirical" (default) | "uniform" | numeric vector | 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. The class relative frequencies are determined by the second data argument of learn.
"uniform"	All class prior probabilities are equal to 1/K, where K is the number of classes.
numeric vector	A numeric vector with one value for each class. Each element is a class prior probability. The component normalizes the elements such that they sum to 1.
structure	A structure S with two fields: S.ClassNames contains a list of the class names. S.ClassProbs contains a vector of corresponding prior probabilities. The component normalizes the elements such that they sum to 1.

Example: c = featureSelectionClassificationReliefFComponent(10,Prior="uniform")

Example: c.Prior = "empirical"

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

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].

• When the maximum feature (predictor) score is a finite value s_max, the software selects a feature with score s if s/s_max is greater than or equal to the RelativeScoreThreshold value.

• When the maximum feature score is infinite, the software selects each feature with an Inf score.

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

Example: c = featureSelectionClassificationReliefFComponent(10, RelativeScoreThreshold=0.5)

Example: c.RelativeScoreThreshold = 0.75

Data Types: single | double

Sigma — Distance scaling factor

Inf (default) | positive integer scalar

Distance scaling factor, specified as a numeric positive scalar. For observation i, influence on the predictor weight from its nearest neighbor j is multiplied by $$e^{-{(\text{rank}(i,j)/\text{sigma)}}^2}$$. rank(i,j) is the position of the jth observation among the nearest neighbors of the ith observation, sorted by distance.

Example: c = featureSelectionClassificationReliefFComponent(10, Sigma=20)

Example: c.Sigma = 50

Data Types: single | double

Updates — Number of observations for computing weights

"all" (default) | positive integer scalar

Number of observations to select at random for computing weights, specified as "all" or a positive integer scalar.

Example: c = featureSelectionClassificationReliefFComponent(10, Updates=25)

Example: c.Updates = "all"

Data Types: single | double | char | string

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

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

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

Example: c = featureSelectionClassificationReliefFComponent(10, Name="FeatureSelector")

Example: c.Name = "ReliefFSelector"

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.

Example: c = featureSelectionClassificationReliefFComponent(10, 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 = featureSelectionClassificationReliefFComponent(10, 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, then the number of tags must match the number of inputs in Inputs.

Example: c = featureSelectionClassificationReliefFComponent(10, 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, then the number of tags must match the number of outputs in Outputs.

Example: c = featureSelectionClassificationReliefFComponent(10, OutputTags=[1 0])

Example: c.OutputTags=[1 2]

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 and Learn Pipeline Component for Classification ReliefF Feature Selection

Create a featureSelectionClassificationReliefFComponent component. Specify to select 3 features using 10 nearest neighbors.

component = featureSelectionClassificationReliefFComponent(10, NumFeatures=3)

component = 
  featureSelectionClassificationReliefFComponent with properties:

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

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

   
Learn Parameters (unlocked)
          NumFeatures: 3
         NumNeighbors: 10


Show all parameters

component is a featureSelectionClassificationReliefFComponent object that contains two learnables: SelectedVariables and UsedVariables. These properties remains 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 = 
  featureSelectionClassificationReliefFComponent with properties:

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

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

   
Learn Parameters (locked)
          NumFeatures: 3
         NumNeighbors: 10


Show all parameters

Note that the HasLearned property is set to true and the SelectedVariables and UsedVariables are nonempty.

Find the names of the selected features.

names = component.SelectedVariables

names = 

  1×3 string array

    "PetalWidth"    "PetalLength"    "SepalLength"

Version History

Introduced in R2026a