Feature Selection with the Boruta Package
This article describes a R package Boruta, implementing a novel feature selection algorithm for finding all relevant variables. The algorithm is designed as a wrapper around a Random Forest classification algorithm. It iteratively removes the features which are proved by a statistical test to be less relevant than random probes. The Boruta package provides a convenient interface to the algorithm. The short description of the algorithm and examples of its application are presented.
Code (1)
Tasks
feature selectionGeneral ClassificationSimilar Papers 제목 키워드 기반
Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection
With the surge in data generation, both vertically (i.e., volume of data) and horizontally (i.e., dimensionality), the burden of the curse of dimensionality has become increasingly palpable. Feature selection, a key face…
Dimensionality Reductionfeature selectionNovel GPU Boruta algorithms for feature selection from high-dimensional data
Most feature selection algorithms, especially wrapper methods, run inefficiently on CPU based platforms because of their high computational complexity. This inefficiency makes them unsuitable for processing large scale d…
Computational EfficiencyFeature ImportanceBoMGene: Integrating Boruta-mRMR feature selection for enhanced Gene expression classification
Feature selection is a crucial step in analyzing gene expression data, enhancing classification performance, and reducing computational costs for high-dimensional datasets. This paper proposes BoMGene, a hybrid feature s…
Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection
Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stoppi…
BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification
Gene expression classification is a pivotal yet challenging task in bioinformatics, primarily due to the high dimensionality of genomic data and the risk of overfitting. To bridge this gap, we propose BOLIMES, a novel fe…
ClassificationDimensionality Reductionfeature selection