Abstract
In recent years, multi-view feature selection has demonstrated significant value in fields such as machine learning and data mining, as it can effectively reduce data dimensionality, remove redundant information, and enhance model performance by leveraging complementary information across multiple views. However, existing algorithms primarily focus on the correlations between features and the target variable within each view, while neglecting the potential causal relationships among features across different views, and thus lack interpretability in their feature selection results. To address these limitations, we propose a Multi-View Causal Feature Selection (MVCFS) algorithm to identify key features with causal interpretability across multiple views. Specifically, MVCFS first uncovers the causal relationships among features across different views based on the local causal structures within each view, and then constructs the complete Markov Blanket of the class variable in the multi-view scenario. Experiments conducted on 12 benchmark datasets demonstrate that MVCFS achieves higher classification accuracy than other multi-view feature selection algorithms on most datasets.