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Article
Computer Science, Artificial Intelligence
Jiawei Yang et al.
Summary: The classification of irregular and complex time-series is difficult, but a new approach called attention entropy that analyzes the frequency distribution of intervals between key observations shows promising results. It outperforms other entropy methods and achieves high accuracy in classifying different groups of people.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
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Xu Tan et al.
Summary: Isolation Forest is widely used for outlier detection in large-scale data due to its low computational complexity. However, it may fail to detect outliers in specific regions due to artifacts caused by the chosen hyperplanes. To address this issue, a random-projection based Isolation Forest is proposed, which transforms the data and improves outlier detection performance.
PATTERN RECOGNITION LETTERS
(2022)
Article
Environmental Sciences
Jiawei Yang et al.
Summary: Trajectory outlier detection is a crucial technique in analyzing GPS trajectory data. Conventional methods have drawbacks, but the MiPo method overcomes these issues and outperforms them in experiments.
Proceedings Paper
Computer Science, Artificial Intelligence
Jiawei Yang et al.
Summary: Most conventional time series outlier detection techniques have limitations for variable-length time series. To overcome this problem, the HK-index is proposed to extract features for any outlier detector from variable-length time series. The HK-index identifies the longest subseries that satisfies certain conditions and uses its length as the feature. It represents the entire time series by utilizing the information of the subseries and ignoring irrelevant information. The HK-index has linear time complexity and is easy to apply.
2022 IEEE 32ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
(2022)
Article
Computer Science, Artificial Intelligence
Zhen Peng et al.
Summary: The text discusses the importance of using attributed networks to model complex systems for anomaly detection research, emphasizing the potential of multi-view data and user preferences in improving detection accuracy. The proposed multi-view framework Alarm incorporates user preferences and addresses heterogeneous attribute characteristics through graph encoders and an aggregator, demonstrating effectiveness in detecting anomalies and supporting user-oriented detection.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jiayuan Li
Summary: In this paper, a polynomial time outlier removal method is proposed, aiming to reduce the input set into a smaller one with a lower outlier rate based on bound principle. A scale-adaptive Cauchy estimator (SA-Cauchy) is introduced for further optimization. Extensive experiments demonstrate that the proposed method is robust at outlier rates above 99 percent and significantly faster than its competitors.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Weixin Luo et al.
Summary: This paper introduces an anomaly detection method based on Deep Neural Networks inspired by sparse coding, with a focus on Temporally-coherent Sparse Coding (TSC) and Sequential Iterative Soft-Thresholding Algorithm (SIATA). The approach utilizes a stacked Recurrent Neural Networks (sRNN) architecture for sparse coefficient optimization and further enhances it with sRNN-AE to improve efficiency and accuracy in anomaly detection. Extensive experiments have shown that sRNN-AE outperforms existing methods, making it an effective tool for anomaly detection.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Automation & Control Systems
Liu Yang et al.
Summary: This article presents a secure clustering protocol for industrial wireless sensor networks, which utilizes fuzzy trust evaluation and outlier detection to ensure security. The protocol adopts an interval type-2 fuzzy logic controller and a density-based outlier detection mechanism, and proposes a fuzzy-based cluster heads election method to achieve a balance between energy saving and security assurance. Extensive experiments demonstrate the effectiveness of the protocol in defending the network against attacks from internal malicious or compromised nodes.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Mehmet Necip Kurt et al.
Summary: Timely detection of abrupt anomalies in high-dimensional data is crucial for real-time monitoring and security of modern systems. The proposed nonparametric algorithms extract useful univariate summary statistics and model anomalies as persistent outliers, demonstrating effectiveness in quick and accurate anomaly detection. By determining acceptance regions for nominal data through Geometric Entropy Minimization, the proposed schemes show promise in high-dimensional settings.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Jiawei Yang et al.
Summary: The mean-shift outlier detector modifies data using mean-shift technique to eliminate the bias caused by outliers and remove their influence without needing to know the outliers. Experimental results show that this method performs well regardless of the number of outliers in the data.
PATTERN RECOGNITION
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zheng Li et al.
20TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2020)
(2020)
Article
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Yezheng Liu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
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Vahid Ranjbar et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2019)
Article
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Jiawei Yang et al.
APPLIED SCIENCES-BASEL
(2019)
Article
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Jin Ning et al.
PATTERN RECOGNITION LETTERS
(2018)
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Computer Science, Artificial Intelligence
Xiaojie Li et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Guilherme O. Campos et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2016)
Article
Computer Science, Information Systems
Fei Tony Liu et al.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2012)
Article
Computer Science, Artificial Intelligence
Yuhua Li et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2011)
Article
Computer Science, Artificial Intelligence
B Schölkopf et al.
NEURAL COMPUTATION
(2001)
Article
Computer Science, Information Systems
S Ramaswamy et al.
