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Your search keyword '"Zhu, Xiaomin"' showing total 412 results
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412 results on '"Zhu, Xiaomin"'

2. A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

Author

Ren, Yingtao, Zhu, Xiaomin, Bai, Kaiyuan, and Zhang, Runtong

Subjects
Computer Science - Artificial Intelligence
Abstract

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitative and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

Published
2024
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3. Recurrent Alignment with Hard Attention for Hierarchical Text Rating

Author

Lin, Chenxi, Ren, Jiayu, He, Guoxiu, Jiang, Zhuoren, Yu, Haiyan, and Zhu, Xiaomin

Subjects
Computer Science - Computation and Language
Abstract

While large language models (LLMs) excel at understanding and generating plain text, they are not tailored to handle hierarchical text structures or directly predict task-specific properties such as text rating. In fact, selectively and repeatedly grasping the hierarchical structure of large-scale text is pivotal for deciphering its essence. To this end, we propose a novel framework for hierarchical text rating utilizing LLMs, which incorporates Recurrent Alignment with Hard Attention (RAHA). Particularly, hard attention mechanism prompts a frozen LLM to selectively focus on pertinent leaf texts associated with the root text and generate symbolic representations of their relationships. Inspired by the gradual stabilization of the Markov Chain, recurrent alignment strategy involves feeding predicted ratings iteratively back into the prompts of another trainable LLM, aligning it to progressively approximate the desired target. Experimental results demonstrate that RAHA outperforms existing state-of-the-art methods on three hierarchical text rating datasets. Theoretical and empirical analysis confirms RAHA's ability to gradually converge towards the underlying target through multiple inferences. Additional experiments on plain text rating datasets verify the effectiveness of this Markov-like alignment. Our data and code can be available in https://github.com/ECNU-Text-Computing/Markov-LLM., Comment: Accepted by EMNLP 2024 Main Conference

Published
2024

4. GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training

Author

Li, Yang, Zhao, Qi'ao, Lin, Chen, Zhang, Zhenjie, and Zhu, Xiaomin

Subjects
Computer Science - Information Retrieval
Abstract

Recommendation with side information has drawn significant research interest due to its potential to mitigate user feedback sparsity. However, existing models struggle with generalization across diverse domains and types of side information. In particular, three challenges have not been addressed, and they are (1) the diverse formats of side information, including text sequences. (2) The diverse semantics of side information that describes items and users from multi-level in a context different from recommendation systems. (3) The diverse correlations in side information to measure similarity over multiple objects beyond pairwise relations. In this paper, we introduce GENET (Generalized hypErgraph pretraiNing on sidE informaTion), which pre-trains user and item representations on feedback-irrelevant side information and fine-tunes the representations on user feedback data. GENET leverages pre-training as a means to prevent side information from overshadowing critical ID features and feedback signals. It employs a hypergraph framework to accommodate various types of diverse side information. During pre-training, GENET integrates tasks for hyperlink prediction and self-supervised contrast to capture fine-grained semantics at both local and global levels. Additionally, it introduces a unique strategy to enhance pre-training robustness by perturbing positive samples while maintaining high-order relations. Extensive experiments demonstrate that GENET exhibits strong generalization capabilities, outperforming the SOTA method by up to 38% in TOP-N recommendation and Sequential recommendation tasks on various datasets with different side information.

Published
2023

8. Global Carleman Estimate and State Observation Problem for Ginzburg-Landau Equation

Author

Dou, Fangfang, Liao, Zhonghua, and Zhu, Xiaomin

Subjects
Mathematics - Analysis of PDEs
Abstract

In this paper, we prove a global Carleman estimate for the complex Ginzburg- Landau operator with a cubic nonlinear term in a bounded domain of Rn, n = 2, 3. As applications, we study state observation problems for the Ginzburg-Landau equation.

Published
2022

27. Group Simulated Annealing Algorithm for ICSI-OC 2022

Author

Zou, Mingyin, Zhang, Peichen, Zhu, Xiaomin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tan, Ying, editor, Shi, Yuhui, editor, and Luo, Wenjian, editor

Published
2023
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30. Phacogoniotomy versus phacotrabeculectomy for advanced primary angle-closure glaucoma with cataract: A randomized non-inferiority trial

Author

Song, Yunhe, Lin, Fengbin, Lv, Aiguo, Zhang, Yao, Lu, Lan, Xie, Lin, Tang, Guangxian, Yuan, Huiping, Yang, Yangfan, Xu, Jiangang, Lu, Ping, Xiao, Meichun, Zhu, Xiaomin, Yan, Xiaowei, Song, Wulian, Li, Xiaoyan, Zhang, Hengli, Li, Fei, Wang, Zhenyu, Jin, Ling, Gao, Xinbo, Liang, Xiaohong, Zhou, Minwen, Zhao, Xiaohuan, Zhang, Yu, Chen, Weirong, Wang, Ningli, Tham, Clement C., Barton, Keith, Park, Ki Ho, Aung, Tin, Weinreb, Robert N., Tang, Li, Fan, Sujie, Lam, Dennis S.C., and Zhang, Xiulan

Published
2024
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39. Influence of Goniotomy Size on Treatment Safety and Efficacy for Primary Open-Angle Glaucoma: A Multicenter Study

Author

Zhang, Yu, Yu, Ping, Zhang, Yingzhe, Sugihara, Kazunobu, Zhu, Xiaomin, Zhang, Yao, Yang, Xiao, Li, Xiaoyan, Liu, Yacong, Zhang, Hengli, Yan, Xiaowei, Zhang, Hengkai, Lin, Fengbin, Song, Yunhe, Gao, Xinbo, Yuan, Huiping, Tang, Guangxian, Zhou, Wenzong, Fan, Sujie, Dang, Guangfu, Tang, Li, Chen, Weirong, Wang, Ningli, Park, Ki Ho, Barton, Keith, Aung, Tin, Lam, Dennis S.C., Weinreb, Robert N., Xie, Lin, Tanito, Masaki, Zeng, Liuzhi, and Zhang, Xiulan

Published
2023
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47. Clinical use of mood stabilizers beyond treatment for bipolar disorder: The REAP-MS study

Author

Chen, Chih-Ken, Yang, Shu-Yu, Park, Seon-Cheol, Jang, Ok-Jin, Zhu, Xiaomin, Xiang, Yu-Tao, Ouyang, Wen-Chen, Javed, Afzal, Khan, M. Nasar Sayeed, Grover, Sandeep, Avasthi, Ajit, Kallivayalil, Roy Abraham, Chee, Kok Yoon, Chemi, Norliza, Kato, Takahiro A., Hayakawa, Kohei, Pariwatcharakul, Pornjira, Maramis, Margarita, Seneviratne, Lakmi, Sim, Kang, Tang, Wai Kwong, Oo, Tin, Sartorius, Norman, Tan, Chay-Hoon, Chong, Mian-Yoon, Park, Yong Chon, Shinfuku, Naotaka, and Lin, Shih-Ku

Published
2023
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