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Hybrid Sequential Feature Selection (HSFS)

Source: R/SequentialSelector.R
SequentialSelector.Rd

Implements a multi-stage feature reduction pipeline to handle high-dimensional omics data efficiently. The pipeline chains: 1. Variance thresholding (remove near-zero variance) 2. Univariate filtering (ANOVA F-test) 3. RF importance filtering (single-pass approximation of RFE) 4. LASSO regularization for final selection

Note: The RF importance stage is a single-pass approximation using Random Forest variable importance, not true iterative Recursive Feature Elimination.

This hybrid approach outperforms single-method selection for high-dimensional data.

References

Thelagathoti et al. "Hybrid feature selection approaches for machine learning"

Public fields

stages

List of selection stages with parameters

selected_features

Features selected at each stage

verbose

Whether to print progress messages

Methods

Public methods

Method new()

Create a new SequentialSelector

Usage

SequentialSelector$new(
  variance_threshold = 0.01,
  univariate_n = 5000,
  univariate_method = "anova",
  rfe_n = 1000,
  lasso_n = NULL,
  verbose = TRUE
)

Arguments

variance_threshold

Minimum variance to keep a feature (default: 0.01)

univariate_n

Number of features after univariate filtering (default: 5000)

univariate_method

Univariate method: "anova", "kruskal", "correlation"

rfe_n

Number of features after RFE (default: 1000)

lasso_n

Target number of features after LASSO (default: NULL = auto)

verbose

Print progress messages

Returns

A new SequentialSelector object

Method create_graph()

Create an mlr3pipelines Graph for the sequential selection

Usage

SequentialSelector$create_graph(task = NULL)

Arguments

task

An mlr3 task to determine feature types

Returns

A Graph object that can be used in a GraphLearner

Method create_learner()

Create a complete GraphLearner with HSFS and a final classifier

Usage

SequentialSelector$create_learner(
  model = "ranger",
  impute_method = "median",
  scale = TRUE
)

Arguments

model

Final classifier: "ranger", "xgboost", "glmnet", etc.

impute_method

How to handle missing values: "median", "mean"

scale

Whether to scale features

Returns

A GraphLearner ready for training/benchmarking

Method print()

Print summary of the selector configuration

Usage

SequentialSelector$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

SequentialSelector$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.