Multi-Label Classification via Adaptive Resonance Theory-Based Clustering
Multi-Label Classification via Adaptive Resonance Theory-Based Clustering
复制标题
DOI：
10.1109/tpami.2022.3230414
复制
发表时间：
2021-03
影响因子：
23.6
通讯作者：
Naoki Masuyama;Y. Nojima;C. Loo;H. Ishibuchi
中科院分区：
文献类型：
--
作者：
Naoki Masuyama;Y. Nojima;C. Loo;H. Ishibuchi
本文提出了一种多标签分类算法，能够通过应用自适应共振理论（ART）为基础的聚类算法和贝叶斯方法的标签概率计算的持续学习。基于ART的聚类算法自适应地连续生成与给定数据相对应的原型节点，并将生成的节点作为分类器。标签概率计算独立地计算每个类别的标签出现次数并计算贝叶斯概率。因此，标签概率计算可以科普标签数量的增加。在人工和真实多标签数据集上的实验结果表明，该算法在实现持续学习的同时，具有与其他已知算法相当的分类性能。
This article 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.