Multi-label Classification via Adaptive Resonance Theory-based Clustering

TL;DR

This paper introduces a multi-label classification method that combines ART-based clustering with Bayesian label probability estimation, enabling continual learning and handling increasing label sets effectively.

Contribution

It presents a novel ART-based clustering algorithm integrated with Bayesian probability for multi-label classification that supports continual learning and adapts to growing label sets.

Findings

Competitive classification performance on synthetic datasets

Effective handling of increasing label numbers

Supports continual learning in multi-label classification

Abstract

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.