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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 35 / No. 10: AAAI-21 Technical Tracks 10

PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector

February 1, 2023

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Authors

Chuan Luo

Microsoft Research, China


Pu Zhao

Microsoft Research, China


Chen Chen

Microsoft Research, China Microsoft 365, United States


Bo Qiao

Microsoft Research, China


Chao Du

Microsoft Research, China


Hongyu Zhang

The University of Newcastle, Australia


Wei Wu

L3S Research Center, Leibniz University Hannover, Germany


Shaowei Cai

State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China School of Computer Science and Technology, University of Chinese Academy of Sciences, China


Bing He

State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China School of Computer Science and Technology, University of Chinese Academy of Sciences, China


Saravanakumar Rajmohan

Microsoft 365, United States


Qingwei Lin

Microsoft Research, China


DOI:

10.1609/aaai.v35i10.17064


Abstract:

Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.

Topics: AAAI

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HOW TO CITE:

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector Proceedings of the AAAI Conference on Artificial Intelligence (2021) 8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector AAAI 2021, 8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin (2021). PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. Proceedings of the AAAI Conference on Artificial Intelligence, 8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. Proceedings of the AAAI Conference on Artificial Intelligence 2021 p.8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. 2021. PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. "Proceedings of the AAAI Conference on Artificial Intelligence". 8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. (2021) "PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector", Proceedings of the AAAI Conference on Artificial Intelligence, p.8784-8792

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin, "PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector", AAAI, p.8784-8792, 2021.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. "PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector". Proceedings of the AAAI Conference on Artificial Intelligence, 2021, p.8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. "PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector". Proceedings of the AAAI Conference on Artificial Intelligence, (2021): 8784-8792.

Chuan Luo||Pu Zhao||Chen Chen||Bo Qiao||Chao Du||Hongyu Zhang||Wei Wu||Shaowei Cai||Bing He||Saravanakumar Rajmohan||Qingwei Lin. PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector. AAAI[Internet]. 2021[cited 2023]; 8784-8792.


ISSN: 2374-3468


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