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Automation & Control Systems
Dejene M. Sime, Guotai Wang, Zhi Zeng, Wei Wang, Bei Peng
Summary: In this article, a novel method for semisupervised defect segmentation based on pairwise similarity map consistency and ensemble-based cross pseudolabels is proposed. It achieved significant performance improvement over the baseline and current state-of-the-art methods, and demonstrated state-of-the-art results on three different datasets.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Rui Chen, Yongqiang Tang, Yuan Xie, Wenlong Feng, Wensheng Zhang
Summary: In this article, a semisupervised progressive representation learning approach, called SPDMC, is proposed for deep multiview clustering. The approach utilizes a flexible and unified regularization method to make full use of the discriminative information contained in prior knowledge. Additionally, the self-paced learning paradigm is introduced to handle the complexity and diversity of multiview representations, resulting in improved clustering performance.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Mathematics
Muhammad Zafran Muhammad Zaly Shah, Anazida Zainal, Taiseer Abdalla Elfadil Eisa, Hashim Albasheer, Fuad A. Ghaleb
Summary: Data stream mining involves processing large amounts of data in nonstationary environments, where the relationship between the data and labels often changes. To adapt to the nonstationarity, concept drift detectors are used to monitor error rates and adjust to the current state. However, current approaches assume fully labeled data, which is impractical. This study proposes a concept drift detection method based on high confidence prediction regions and an ensemble-based adaptation approach.
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Computer Science, Artificial Intelligence
Siyu Huang, Tianyang Wang, Haoyi Xiong, Bihan Wen, Jun Huan, Dejing Dou
Summary: This study presents a novel deep active learning approach that utilizes temporal output discrepancy to estimate sample loss and select informative unlabeled samples. The method is efficient, flexible, and task-agnostic, demonstrating superior performance in image classification and semantic segmentation tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xu Chen, Brett Wujek
Summary: In this paper, we propose a novel unified framework called AutoDAL for automated distributed active learning to address multiple challenging problems in active learning. The framework is able to handle limited labeled data, imbalanced datasets, automatic hyperparameter selection, and scalability to big data. Experimental results show that the proposed AutoDAL algorithm achieves significantly better performance compared to several state-of-the-art AutoML approaches and active learning algorithms.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jie Zhao, Yong Deng
Summary: This article presents a novel model of evidence theory based on complex networks, addresses some typical issues of evidence theory, and introduces a new combination rule.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Biology
Hongtuo Lin, Chufan Jian, Yang Cao, Xiaoguang Ma, Hailiang Wang, Fen Miao, Xiaomao Fan, Jinzhu Yang, Gansen Zhao, Hui Zhou
Summary: Major depressive disorder is a common mental illness that may lead to suicide behaviors when severe. Current automatic MDD detection methods in clinical settings rely heavily on EEG signals and supervised learning techniques, facing challenges of subjective data labeling and high cost. The proposed MDD-TSVM method addresses these issues effectively and outperforms existing techniques in identifying MDD patients.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
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Computer Science, Artificial Intelligence
Yingjie Tian, Yuqi Zhang
Summary: This paper provides a comprehensive examination of regularization strategies in machine learning, emphasizing the importance of improving model generalization ability and the need to choose appropriate regularization techniques for specific tasks. Opportunities and challenges in regularization technologies are discussed, along with potential research trends and open concerns.
INFORMATION FUSION
(2022)
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Computer Science, Artificial Intelligence
YunLong Mi, Wenqi Liu, Yong Shi, Jinhai Li
Summary: In human concept learning, semi-supervised learning combines labeled and unlabeled data, and this approach needs to be redesigned for new data input. This study proposes a novel method for dynamic semi-supervised learning using concept spaces and structures, which mimics human cognitive processes.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Review
Pathology
Ewen D. McAlpine, Pamela Michelow, Turgay Celik
Summary: This article introduces the concept of unsupervised learning and discusses how clustering, generative adversarial networks (GANs), and autoencoders can address the lack of annotated data in anatomic pathology.
AMERICAN JOURNAL OF CLINICAL PATHOLOGY
(2022)
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Engineering, Electrical & Electronic
Zhaorui Zhu, Quanxue Gao
Summary: With the increasing interest in multi-view clustering due to the diversity of data modalities, this paper proposes a valid semi-supervised multi-view spectral clustering algorithm. By incorporating prior knowledge, utilizing tensor minimization, and applying cannot-link constraints, the algorithm outperforms current methods in terms of stability and accuracy. Experimental results on various datasets demonstrate the algorithm's effectiveness and potential applications.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Rodrigo G. F. Soares, Leandro L. Minku
Summary: Learning from data streams in nonstationary environments is important and challenging. Existing approaches rely on labeled data to handle concept drifts, which is expensive. We propose a novel algorithm that uses unlabeled data to tackle concept drifts and learns meaningful data representations with an online semisupervised neural network.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xiaoli Zhao, Minping Jia, Zheng Liu
Summary: The article introduces an intelligent fault diagnosis method for rotating machinery based on semisupervised deep sparse auto-encoder (SSDSAE) with local and nonlocal information. Vibration spectrum signals are fed into the SSDSAE algorithm for fault feature extraction, and the extracted sparse discriminant features are used for fault diagnosis with a back-propagation (BP) classifier. The method utilizes weighted cross-entropy (WCE) techniques to improve the generalization performance of the fault diagnosis model and is validated with experimental data.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Artificial Intelligence
Tao Zhang, Tianqing Zhu, Jing Li, Mengde Han, Wanlei Zhou, Philip Yu
Summary: This paper explores the use of semi-supervised learning to address fairness issues in machine learning, including predicting labels for unlabeled data, resampling to obtain multiple fair datasets, and using ensemble learning to improve accuracy and reduce discrimination. Theoretical analysis and experiments demonstrate that this method achieves a better trade-off between accuracy and fairness.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Information Systems
Yiyang Yang, Sucheng Deng, Juan Lu, Yuhong Li, Zhiguo Gong, Leong U. Hou, Zhifeng Hao
Summary: This paper proposes a framework called GraphLSHC to address the scalability issue faced by large-scale hypergraph spectral clustering. The framework expands the hypergraph into a general format, partitions vertices and hyperedges simultaneously to reduce computational complexity, and improves performance through hyperedge-based sampling techniques. Numerous experiments demonstrate the superiority of the proposed framework over existing algorithms.
INFORMATION SCIENCES
(2021)
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Guofu Zhang, Zhaopin Su, Miqing Li, Meibin Qi, Jianguo Jiang, Xin Yao
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
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Computer Science, Theory & Methods
Tao Chen, Rami Bahsoon, Xin Yao
ACM COMPUTING SURVEYS
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Tao Chen, Ke Li, Rami Bahsoon, Xin Yao
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2018)
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Computer Science, Artificial Intelligence
Chao Qian, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao, Zhi-Hua Zhou
EVOLUTIONARY COMPUTATION
(2018)
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Computer Science, Artificial Intelligence
Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)
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Engineering, Mechanical
He Ma, Ziyang Li, Mohamad Tayarani, Guoxiang Lu, Hongming Xu, Xin Yao
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Zhichen Gong, Huanhuan Chen, Bo Yuan, Xin Yao
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
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Engineering, Marine
Yuntao Dai, Ran Cheng, Xin Yao, Liqiang Liu
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Computer Science, Artificial Intelligence
Borhan Kazimipour, Mohammad Nabi Omidvar, A. K. Qin, Xiaodong Li, Xin Yao
APPLIED SOFT COMPUTING
(2019)
Editorial Material
Computer Science, Artificial Intelligence
Shuo Wang, Leandro L. Minku, Nitesh Chawla, Xin Yao
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Ke Li, Renzhi Chen, Dragan Savic, Xin Yao
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2019)
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Wenjing Hong, Ke Tang, Aimin Zhou, Hisao Ishibuchi, Xin Yao
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Computer Science, Artificial Intelligence
Ran Cheng, Mohammad Nabi Omidvar, Amir H. Gandomi, Bernhard Sendhoff, Stefan Menzel, Xin Yao
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Computer Science, Artificial Intelligence
Chaoyue Wang, Chang Xu, Xin Yao, Dacheng Tao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
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Computer Science, Artificial Intelligence
Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xin Yao
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2019)