Residual diverse ensemble for long-tailed multi-label text classification

扫码查看

原文链接

万方数据

外文摘要：Long-tailed multi-label text classification aims to identify a subset of relevant labels from a large candidate label set,where the training datasets usually follow long-tailed label distributions.Many of the previous studies have treated head and tail labels equally,resulting in unsatisfactory performance for identifying tail labels.To address this issue,this paper proposes a novel learning method that combines arbitrary models with two steps.The first step is the"diverse ensemble"that encourages diverse predictions among multiple shallow classifiers,particularly on tail labels,and can improve the generalization of tail labels.The second is the"error correction"that takes advantage of accurate predictions on head labels by the base model and approximates its residual errors for tail labels.Thus,it enables the"diverse ensemble"to focus on optimizing the tail label performance.This overall procedure is called residual diverse ensemble(RDE).RDE is implemented via a single-hidden-layer perceptron and can be used for scaling up to hundreds of thousands of labels.We empirically show that RDE consistently improves many existing models with considerable performance gains on benchmark datasets,especially with respect to the propensity-scored evaluation metrics.Moreover,RDE converges in less than 30 training epochs without increasing the computational overhead.

外文关键词：

multi-label learningextreme multi-label learninglong-tailed distributionmulti-label text clas-sificationensemble learning

作者：

Jiangxin SHI、Tong WEI、Yufeng LI

展开 >

作者单位：

National Key Laboratory for Novel Software Technology,Nanjing University,Nanjing 210023,China

School of Artificial Intelligence,Nanjing University,Nanjing 210023,China

School of Computer Science and Engineering,Southeast University,Nanjing 210096,China

Key Laboratory of Computer Network and Information Integration,Southeast University,Ministry of Education,Nanjing 210096,China

展开 >

出版年：

2024

DOI：

10.1007/s11432-022-3915-6

中国科学:信息科学(英文版)

中国科学院

中国科学:信息科学(英文版)

CSTPCDEI

影响因子：0.715

ISSN：1674-733X

年,卷(期)：2024.67(11)