Dynamic Recursive Fuzzy ART Multi-label Classifier
Yanwu Ren, Marek Reformat · 2025
A new approach based on the Fuzzy Adaptive Resonance Theory (ART) network, called the Dynamic Recursive Fuzzy ART Classifier (DyRFAC), is presented for incremental supervised learning on multi-label datasets. Whereas many ART-based algorithms rely on a single, unvarying vigilance parameter, our classifier employs a dynamic vigilance mechanism, enabling finer-grained partitioning of the data space. Through recursive splitting guided by a purity measure, DyRFAC iteratively adjusts category boundaries, allowing the category space to more accurately capture the data distribution. In addition, DyRFAC introduces a category merging procedure to control the growth of categories when data scales, preventing excessive proliferation that could degrade model performance. We conduct experiments comparing DyRFAC with other multi-label classification algorithms, demonstrating its competitive performance on complex label distributions.