A Conditional Mutual Information-Based Approach for Robust Multi-Source Feature Selection in IoT Systems

Feature selection is essential for high-dimensional multi-source feature analysis, particularly in Internet of Things (IoT) environments characterized by data heterogeneity, redundancy, and noise. To address the need to balance classification performance, dimensionality reduction, and selection stability, this study proposes a residual-based conditional mutual information and feedback fusion (RCMF) feature-selection method. Inspired by the idea of conditional mutual information, the proposed method first introduces a residual-based indicator to characterize the incremental discriminative information retained by a candidate feature under given conditional constraints. On this basis, model-driven predictive contribution and stability score are further incorporated, and the weights of different evaluation components are iteratively updated during the feature-selection process to achieve adaptive fusion. In this way, the method jointly considers conditional discriminative information, task relevance, and selection consistency within a unified feature-evaluation procedure. Experiments on multiple publicly available benchmark and IoT-related datasets validate the rationality and effectiveness of the proposed method.