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Open Access

ARTICLE

A Multi-Tree Genetic Programming Framework for Multi-View Feature Construction and Fusion

Dongmin Zhang1,#, Chao Sun1,#, Yikun Zhang2,*, Runyao Yin2, Chen Chen1
1 Southwest China Institute of Electronic Technology, Chengdu, China
2 School of Future Science and Engineering, Soochow University, Suzhou, China
* Corresponding Author: Yikun Zhang. Email: email
# These authors contributed equally to this work and share first authorship

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086570

Received 02 June 2026; Accepted 28 July 2026; Published online 10 September 2026

Abstract

Multi-view tabular data poses challenges due to heterogeneous feature spaces, divergent distributions, and implicit cross-view relationships. Traditional methods further struggle because their hand-crafted fusion strategies cannot adequately capture complex nonlinear interactions among views. To address this issue, this paper proposes a genetic programming (GP)-based multi-view feature fusion method. It employs a multi-tree GP framework: each view is assigned a dedicated tree for intra-view feature selection and construction, and a fusion tree combines their outputs for cross-view feature-level fusion. An enhanced feature construction strategy further enriches the final representation by exploiting subtree information. Together with decision-level fusion, GP-based multi-view feature fusion framework (GPMVF) forms a two-level fusion framework that jointly handles intra-view construction, cross-view fusion, and classification. Experiments on eight public multi-view datasets show that the proposed method achieves better performance than the compared baseline method.

Keywords

Genetic programming; multi-view learning; feature construction; feature fusion

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