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Journals / Yenilikçi Mühendislik ve Doğa Bilimleri / 2025 / Volume: 5 Issue: 2

PWFS: A scalable parallel Python module for wrapper feature selection

Hakan Alp Eren(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0001-6105-158X
Savaş Okyay(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0003-3955-6324
Nihat Adar(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0002-0555-0701
Pages
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Publication Date
2025-07-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

An overall performance gain of 19.77% is achieved with hyper-threading.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the field of machine learning, the feature selection process is a crucial step, and it can significantly impact the performance of predictive models.
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Key Findings & Data:The framework promises maximum benefit on the machine learning side by empowering parallel performance and efficiency.
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Conclusion & Impact:An overall performance gain of 19.77% is achieved with hyper-threading.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Parallel programming · Open-source software · Feature selection

Abstract

In the field of machine learning, the feature selection process is a crucial step, and it can significantly impact the performance of predictive models. Despite the existence of various time-efficient algorithms, the only method that guarantees problem optimization is exhaustive search, but it requires an enormous computational load. Although the exhaustive search ensures the best feature selection, a lifetime would not be enough after certain large feature counts. This study proposes a generic, scalable open-source parallel Python module to find the best wrapper feature subset in a fully optimized execution time, especially for reasonable feature counts. This parallel wrapper feature selection module, PWFS, is independent of machine learning algorithms and can function with user-defined methods. The framework promises maximum benefit on the machine learning side by empowering parallel performance and efficiency. The system design is built on the most efficient message-passing communication, where the framework distributes the computational load equally among the parallel agents via feature masking. The module is validated on two workstations, one of which is hyper-threading capable. An overall performance gain of 19.77% is achieved with hyper-threading. Various scenarios and experiments yield different speedups and efficiencies up to 96.74%, validating the flexible design of the proposed parallel framework. The source code of the module is available at https://github.com/haeren/parallel-feature-selector and https://pypi.org/project/parallel-feature-selector/.

Özet

In the field of machine learning, the feature selection process is a crucial step, and it can significantly impact the performance of predictive models. Despite the existence of various time-efficient algorithms, the only method that guarantees problem optimization is exhaustive search, but it requires an enormous computational load. Although the exhaustive search ensures the best feature selection, a lifetime would not be enough after certain large feature counts. This study proposes a generic, scalable open-source parallel Python module to find the best wrapper feature subset in a fully optimized execution time, especially for reasonable feature counts. This parallel wrapper feature selection module, PWFS, is independent of machine learning algorithms and can function with user-defined methods. The framework promises maximum benefit on the machine learning side by empowering parallel performance and efficiency. The system design is built on the most efficient message-passing communication, where the framework distributes the computational load equally among the parallel agents via feature masking. The module is validated on two workstations, one of which is hyper-threading capable. An overall performance gain of 19.77% is achieved with hyper-threading. Various scenarios and experiments yield different speedups and efficiencies up to 96.74%, validating the flexible design of the proposed parallel framework. The source code of the module is available at https://github.com/haeren/parallel-feature-selector and https://pypi.org/project/parallel-feature-selector/.

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Cite This Article

Eren, H. A., Okyay, S., Adar, N. (2025). PWFS: A scalable parallel Python module for wrapper feature selection. Yenilikçi Mühendislik ve Doğa Bilimleri, Volume: 5 Issue: 2. https://doi.org/10.61112/jiens.1639780

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