Online Heterogeneous Feature Selection

Many real-world datasets contain high-dimensional heterogeneous features, exhibiting complex and evolving distributions. The coexistence of high dimensionality and heterogeneity poses challenges for reliable feature selection and real-time analysis, while most existing feature selection solutions either assume that the features are of the same type or struggle to handle extremely high-dimensional features. Moreover, these methods are usually designed for static datasets, neglecting the dynamic capture of heterogeneous interfeature relationships in real-time environments. To address these challenges, we propose a new feature selection method called graph-unified adaptive decision boundary enhancement (GRADE) for online heterogeneous feature selection (OHFS). To provide a reliable foundation for evaluating feature subsets under dynamic and heterogeneous data streams, an incremental graph-unified metric (IGUM) is introduced. It mitigates information loss between heterogeneous features by leveraging graph structures to unify feature-value-level and interfeature-level relationships. With such a consistent relation measure, an adaptive density-guided neighborhood relation (ADNR) is proposed to assess the capability of selected feature subsets to classify samples. Since it dynamically captures prominent neighborhood regions, local decision boundaries can thus be precisely delineated. It turns out that GRADE can obtain a more concise feature subset while achieving competitive classification accuracy. Besides, GRADE is parameter-free and very efficient compared with state-of-the-art methods. Comprehensive experimental evaluations, including significance tests, ablation studies, efficiency evaluation, and case studies, have been conducted to verify the efficacy of GRADE.