Dynamic Recursive Fuzzy ART Multi-label Classifier

• Published in: IEEE International Fuzzy Systems conference proceedings pp. 1 - 7
• Main Authors: Ren, Yanwu, Reformat, Marek Z.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.07.2025
• Subjects: Aerospace electronics; Clustering algorithms; Data models; Dynamic vigilance; Fuzzy ART; Heuristic algorithms; Indexes; Merging; Multi label classification; Multi-label Classifier; Resonance; Subspace constraints; Supervised learning
• ISSN: 1558-4739
• DOI: 10.1109/FUZZ62266.2025.11152226

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.