Continuous-Aware Multi-view Attribute Weighted Naive Bayes

Attribute weighting provides a flexible way to alleviate the conditional independence assumption in naive Bayes. Within this widely studied area, multi-view attribute weighting has emerged as a new paradigm and achieved state-of-the-art performance by exploiting complementary information from multiple data representations. However, existing multi-view attribute weighting methods are primarily designed for nominal attributes, whereas continuous attributes are more prevalent in real-world applications. Although discretization enables these methods to handle continuous attributes, it removes differences between values within the same interval, limiting their ability to capture fine-grained continuous information. To address this issue, we propose a novel method called Continuous-aware Multi-view Attribute Weighted Naive Bayes (CMAWNB), which directly models continuous attributes by constructing three additional views from the perspective of complex attribute dependencies, continuous space partitions, and local group structures, respectively. Subsequently, attribute weights are independently optimized within each view, and the view-specific predictions are fused for classification. Experiments on 55 benchmark continuous datasets demonstrate the effectiveness of CMAWNB.