.png){kind=icon}
返回
BoostXML: Gradient Boosting for Extreme Multilabel Text Classification With Tail Labels
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3285294.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multilabel learning involving hundreds of thousands or even millions of labels is referred to as extreme multilabel learning (XML), in which the labels often follow a powerlaw distribution with the majority occurring in very few data points as tail labels. Recent years have witnessed the intensive use of deep-learning methods for high-performance XML, but they are typically optimized for the head labels with abundant training instances and less consider the performance on tail labels, which, however, like the needles in haystacks, are often the focus of attention in real-life applications. In light of this, we present BoostXML, a deep learning-based XML method for extreme multilabel text classification, enhanced greatly by gradient boosting. In BoostXML, we pay more attention to tail labels in each Boosting Step by optimizing the residual mostly from unfitted training instances with tail labels. A Corrective Step is further proposed to avoid the mismatching between the text encoder and weak learners during optimization, which reduces the risk of falling into local optima and improves model performance. A Pretraining Step is also introduced in the initial stage of BoostXML to avoid exorbitant bias to tail labels. Extensive experiments on five benchmark datasets with stateof-the-art baselines demonstrate the advantage of BoostXML in tail-label prediction.
Keyword:
Corrective step
deep learning
extreme multilabel learning (XML)
gradient boosting
tail labels
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
分享收藏AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi -class imbalanced datasets using transfer learningAdaboost-cnn: 卷积神经网络的自适应boosting算法，用于使用迁移学习对多类不平衡数据集进行分类.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏分享收藏Definitive evidence that a single N-glycan among three glycans on inducible costimulator is required for proper protein trafficking and ligand binding明确的证据表明，在可诱导的共刺激物上的三个聚糖中，单个N-聚糖对于适当的蛋白质运输和配体结合是必需的
PREAI
分享收藏分享收藏Research on the Efficiency of Wireless Power Transfer System Based on Multi-Auxiliary Transmitting Coils基于多辅助发射线圈的无线电能传输系统效率研究
PREAI
分享收藏Selective thrombophilia screening of patients with nonarteritic anterior ischemic optic neuropathy
PREAI
分享收藏
查看更多内容