# Multi-label Classification via Adaptive Resonance Theory-based Clustering

arXiv: https://arxiv.org/abs/2103.01511 · HTML: https://mlsanity.com/paper/2103.01511/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 63 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Multi-Label Learning with Global and Local Label Correlation](https://mlsanity.com/paper/1704.01415/) (2017)
- [Survey of state-of-the-art mixed data clustering algorithms](https://mlsanity.com/paper/1811.04364/) (2018)
- [Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence](https://mlsanity.com/paper/1901.00794/) (2018)

## Built on by (most cited first, 7 of 7)

- [Measuring the Validity of Clustering Validation Datasets](https://mlsanity.com/paper/2503.01097/) (2025)
- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)
- [Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory](https://mlsanity.com/paper/2309.03487/) (2023)
- [An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter](https://mlsanity.com/paper/2511.17983/) (2025)
- [Adaptive Growth: Real-time CNN Layer Expansion](https://mlsanity.com/paper/2309.03049/) (2023)
- [OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance](https://mlsanity.com/paper/2511.01320/) (2025)
- [PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering](https://mlsanity.com/paper/2605.08673/) (2026)

## Abstract (arXiv)

This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.