Leveraging Fuzzy Manifold Intra-Class Correlation and Inter-Class Separability for Online Multilabel Streaming Features Analysis

Traditional multilabel feature selection (MFS) typically relies on pre-computing global information within the feature space. However, in real-world applications, features are dynamically generated and continuously arrive over time, known as streaming features, rendering many existing approaches ineffective. Some MFS methods for streaming features have been developed, several challenges persist: (1) Previous research often uses certain strategies to model streaming feature evaluation, failing to process fuzzy information effectively; (2) The maximum correlation between features and class is emphasized, while inter-class separability is ignored, leading to inaccurate feature evaluation; (3) The continuous influx of streaming features brings the dynamics and unknowns to data distribution, has been largely overlooked in previous work; (4) Streaming feature selection requires immediate feedback on newly arriving features, posing challenges to the algorithm's real-time responsiveness. Motivated by these observations, this paper introduces a novel online MFS strategy for streaming features. First, the weighted manifold distance is designed, and the fuzzy manifold similarity learning strategy is formalized to analyze the instance relationships of unknown distribution. Second, the fuzzy manifold intra-class correlation and inter-class separability are devised to quantify feature discriminability. Finally, a novel multilabel streaming feature analysis framework is established, with feature discriminability as the guiding factor. Incoming features are categorized as weakly relevant, strongly relevant, or redundant, culminating in generating a reliable feature selection subset. Extensive experiments on fifteen public datasets demonstrate that our algorithm achieves competitive performance compared to nine state-of-the-art offline and online algorithms.