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New benchmark SelectArena evaluates feature selection methods on real-world data

By PulseAugur Editorial · [1 sources] ·

A new benchmark called SelectArena has been introduced to address the limitations of existing feature selection benchmarks in real-world tabular data applications. SelectArena compares 15 feature selection methods across 71 real-world datasets using standardized protocols and state-of-the-art tabular models. The findings highlight trade-offs between predictive performance, runtime, stability, and validity, suggesting that tree-based methods like CART and RFImportance, along with F-Test, are Pareto-optimal for performance and runtime. F-Test also emerges as a balanced choice for stability and validity. AI

IMPACT Provides practical guidance for selecting features in tabular data, potentially improving model efficiency and interpretability.

RANK_REASON The item is a research paper introducing a new benchmark for evaluating machine learning methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark SelectArena evaluates feature selection methods on real-world data

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The item is a research paper introducing a new benchmark for evaluating machine learning methods. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bastian Sch\"afer, Dominika Matus, Lennart Purucker, Ricardo Knauer, Erik Rodner, Frank Hutter, Anton Sokolov, Arnaud Liefooghe ·

    SelectArena: A Modern Benchmark for Feature Selection on Real-world Tabular Data

    arXiv:2610.13644v1 Announce Type: cross Abstract: Selecting a subset of features (columns) from a tabular dataset is a ubiquitous challenge in real-world applications. Industry experts seek to minimize model training costs, climate scientists require an interpretable subset of fa…

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