Probabilistic Label Tree for Streaming Multi-Label Learning

By Tong Wei, Jiang-Xin Shi, Yu-Feng Li. 2021

15 citations recorded by the source corpus.

Multi-label learning aims to predict a subset of relevant labels for each instance, which has many real-world applications. Most extant multi-label learning studies focus on a fixed size of label space. However, in many cases, the environment is open and changes gradually and new labels emerge, which is coined as streaming multi-label learning (SMLL). SMLL poses great challenges in twofolds: (1) the target output space expands dynamically; (2) new labels emerge frequently and can reach a significantly large number. Previous attempts on SMLL leverage label correlations between past and emerging labels to improve the performance, while they are inefficient when deal with large-scale problems. To cope with this challenge, in this paper, we present a new learning framework, i.e., the probabilistic streaming label tree(Pslt). In particular, each non-leaf node of the tree corresponding to a subset of labels, and a binary classifier is learned at each leaf node. Initially, Pslt is learned on partially observed labels, both tree structure and node classifiers are updated while new labels emerge. Using carefully designed updating mechanism, Psltcan seamlessly incorporate new labels by first passing them down from the root to leaf nodes and then update node classifiers accordingly. We provide theoretical bounds for the iteration complexity of tree update procedure and the estimation error on newly arrived labels. Experiments show that the proposed approach improves the performance in comparison with eleven baselines in terms of multiple evaluation metrics. The source code is available at https://gitee.com/pslt-kdd2021/pslt.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liu Liu
2. Dacheng Tao
3. Zhen Wang
4. Yiqun Duan
5. Jing Zhang
6. Charu C. Aggarwal
7. Jun Zhu
8. Hang Su

In Information Systems

1. Dacheng Tao
2. Liu Liu
3. Zhen Wang
4. Charu C. Aggarwal

Citations in the corpus

• Continual Learning through Retrieval and Imagination (2022, background): “Unfortunately, most of the exiting methods fail to satisfy all these guidelines mentioned above (Chaudhry et al. 2021; Wang et al. 2020; Lopez-Paz et al. 2017; Wei et al. 2021; Riemer et al. 2019; Kong et al. 2021; Schwarz et al. 2018; Zhu et al. 2020).”
• Continual Learning with Lifelong Vision Transformer (2022, unknown): “Label-based methods [28,77,80–82 ,”