Multi-view Ensemble Feature Selection via SemiDefinite Programming

Xiaojian Ding, Xin Wang and Pengcheng Shi

European Journal of Operational Research, 2026, vol. 328, issue 1, 269-281

Abstract: Multi-view learning faces significant challenges in selecting discriminative features while managing redundancy and noise across heterogeneous data sources. To address these issues, this paper introduces Multi-view Ensemble Feature Selection (MEFS), a novel framework that systematically integrates view generation (VG) and view selection (VS) through a unified optimization paradigm. By reformulating feature selection as a MaxCut problem and leveraging SemiDefinite Programming (SDP) relaxation, MEFS dynamically balances the generalization capability of individual views with their pairwise diversity, eliminating the need for manual parameter tuning. A key innovation is the proposed pairwise diversity metric, which quantifies inter-view dissimilarity using between-class scatter matrices to ensure complementary feature subsets. Extensive experiments on ten benchmark datasets demonstrate that MEFS consistently outperforms state-of-the-art methods in accuracy, robustness, and computational efficiency. Ablation studies validate the synergistic effect of combining VG and VS modules.

Keywords: Multi-view; Feature selection; Ensemble learning; SemiDefinite programming (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:328:y:2026:i:1:p:269-281

DOI: 10.1016/j.ejor.2025.07.014

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