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.

Crowdsourcing has become a powerful tool for data collection and problem-solving, harnessing collective intelligence to address complex tasks. Building knowledge bases through such intelligence is gaining attention to enhance cross-domain model performance. As an essential technique for dimensionality reduction, the task of feature selection (FS) also emerges in the context of crowdsourcing, resulting in crowd-sourcing feature selection (CFS). Due to the diversity of workers and the varying quality of data in a crowdsourcing environment, CFS faces challenges such as heterogeneous data reliability and dynamic participation. To tackle these issues, we present Crowd-Fed Evolutionary Transfer Optimization (CFETO), an efficient and crowdsourcing-enhanced framework for feature selection. CFETO comprises a central server and multiple distributed workers: workers perform local data collection and optimization, extracting knowledge from their datasets, while the server integrates this knowledge to build a knowledge base. An evolutionary transfer learning strategy is further employed to harness this knowledge, thereby improving both convergence speed and selection robustness. Experiments on 20 real-world datasets show that CFETO outperforms traditional centralized FS methods approaches, underlining its potential for broader application in complex, distributed crowdsourcing scenarios.