@Article{cmc.2026.086570,
AUTHOR = {Dongmin Zhang, Chao Sun, Yikun Zhang, Runyao Yin, Chen Chen},
TITLE = {A Multi-Tree Genetic Programming Framework for Multi-View Feature Construction and Fusion},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/28241},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2026.086570}
}