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Crowdsourcing Feature Selection via a Distributed Evolutionary Algorithm
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AI
DOI：10.1109/tsc.2026.3688040.png)
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Abstract
En 中文
Crowdsourcing leverages the collective intelligence of the crowd to collect data and solve complex computational tasks.Driven by this paradigm, data can now be gathered more efficiently and at larger scales, thereby increasing the need for effective dimensionality reduction, which makes feature selection (FS) essential for efficient learning. In this paper, we refer to the problem in which multiple workers in a crowdsourcing environment collect data and concurrently optimize FS as the crowdsourcing feature selection (CFS) problem. In CFS, workers perform FS on local data and upload candidate feature subsets to complete the outsourced task. Nevertheless, the heterogeneity across multiple data sources hinders the formation of a reliable and high-quality consensus solution. To address this issue, we propose a cooperative learning–based Distributed Evolutionary Algorithm for the CFS problem (DEA-CFS). First, we formulate the CFS problem and define the roles of workers and the server, as well as their interactions in a crowdsourcing environment. Second, on the worker side, we design a distributed cooperative learning strategy that refines local solutions and mitigates data heterogeneity through confidence-aware fitness comparison. Third, on the server side, we introduce an adaptive credibility-based aggregation mechanism that aggregates a robust consensus solution. Extensive experiments on 18 datasets with up to 10,000 features demonstrate the efficiency and effectiveness of DEA-CFS. Specifically, compared to four existing distributed baselines, DEA-CFS achieves a superior average rank of 1.16 and obtains the best performance on 15 of the 18 datasets.
Keywords:
Collaborative learning
crowdsourcing
evolutionary computation
feature selection
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