A Distributed Feature Selection Approach Based on Granular Computing Technique

doi:10.1109/tpds.2026.3743572

A Distributed Feature Selection Approach Based on Granular Computing Technique

2026-10-19

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DOI：10.1109/tpds.2026.3743572

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Feature selection plays a pivotal role in both machine learning and data analysis. The primary goal is to improve classification accuracy and reduce computational costs by identifying a pertinent feature subset. Many feature selection approaches exhibit robust performance on small or medium datasets, but their scalability and efficiency for more complex datasets are limited due to the serial methodology and the single-processor environment. Motivated by this, we propose a distributed feature selection method based on granular computing technique. First, an adaptive information granulation method is applied to original data for the extraction of granulation features. Then, the granular computing-based feature selection approach is established with a relevance and redundancy criterion. Furthermore, the distributed feature selection method is developed in the Map-Reduce architecture for distributed computing environments. The relevant experiments illustrate the feasibility, effectiveness and distributed capability on several benchmark datasets for feature selection tasks.

Keywords:

Distributed algorithm

information granulation

feature selection

relevance and redundancy criterion

A Distributed Feature Selection Approach Based on Granular Computing Technique

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