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Article
Computer Science, Theory & Methods
Matthew J. Vowels et al.
Summary: Causal reasoning is important for science and human intelligence. We need structure discovery methods to uncover causal relationships from data. This review focuses on modern optimization methods and provides references to benchmark datasets and software packages. Finally, the assumptive leap required to bridge structure and causality is discussed.
ACM COMPUTING SURVEYS
(2023)
Review
Computer Science, Artificial Intelligence
Samuel Sousa et al.
Summary: This article systematically reviews over sixty DL methods for privacy-preserving NLP published between 2016 and 2020, covering classification, privacy threats, metrics, and challenges in real-world scenarios.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Juntian Shi et al.
Summary: This paper proposes a novel framework for real-time urban traffic outlier detection, which can comprehensively consider both individual and group outliers, and is effective and efficient.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Yan Qiao et al.
Summary: This paper focuses on anomaly detection for high-dimensional sensing data. The efficiency of existing OCSVM-based methods is compromised when dealing with high-dimensional and large-scale data. Although dimensionality reduction techniques can help, the accuracy and timely detection still face challenges. To address this issue, the authors propose a new form of OCSVM model based on compressed data and OCSVM characteristics, and develop optimal and approximate methods for training and testing the model. Experimental results on real-world datasets demonstrate that the proposed methods outperform state-of-the-art techniques in terms of accuracy and efficiency, without requiring manual parameter tuning.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Carmen Lancho et al.
Summary: This paper proposes a complexity measure that offers a multi-level perspective of data complexity. It estimates complexity by applying the k-means algorithm in a recursive and hierarchical way at the instance level. The instance information is aggregated to provide complexity knowledge at the class and the dataset levels. Experimental results demonstrate that this method is competitive, stable, and robust.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Francisco Melo Pereira et al.
Summary: This paper analyzes two machine learning algorithms, DBSCAN and LOF, applied in the detection of outliers in a continuous framework for PoI detection. The paper provides the functional design for the overall framework and compares the performance of DBSCAN and LOF on different datasets. Results show that LOF exhibits the best performance and is more suitable for outlier detection in real-time PoI detection frameworks.
Article
Computer Science, Information Systems
Duc-Minh Ngo et al.
Summary: This study proposes a hardware-based framework for network intrusion detection using lightweight artificial neural network models. Anomaly-based intrusion detection systems using machine learning have gained popularity due to their ability to detect unseen attacks, but deploying them on IoT devices is computationally expensive. This paper presents a high-performance and ultra-low power consumption framework that achieves high accuracy and faster inference compared to traditional hardware.
Article
Computer Science, Artificial Intelligence
Abhaya Abhaya et al.
Summary: Unsupervised Learning is a widely used approach for outlier detection, but traditional deep learning-based models have issues in detecting outlier points. This paper proposes two techniques to address the problem of reconstruction error and demonstrates the superiority of these techniques through experiments.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Francisco J. Baldan et al.
Summary: This article proposes a new representation method for time series classification based on a robust and complete feature set. The method allows the development of interpretable classifiers and expands the available techniques for this type of problem. Experimental results show competitive performance compared to state-of-the-art models, with no statistically significant differences.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Chao-Han Huck Yang et al.
Summary: Current top-notch deep learning based vision models struggle with noisy data, which arises from various factors such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. This work proposes Treatment Learning Causal Transformer (TLT), a transformer-based architecture that incorporates binary information of noise existence into image classification tasks. TLT estimates robust feature representations and assigns appropriate inference networks based on the estimated noise level, significantly improving prediction accuracy and visual salience methods.
2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
(2023)
Article
Computer Science, Artificial Intelligence
Cheong Hee Park
Summary: This paper compares and analyzes the performance of outlier detection in high dimensional data, with a focus on text data with dimensions typically in the tens of thousands. The performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions are compared through simulated experimental setups. The paper also discusses the use of k-NN distance in high dimensional data.
JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Jinpeng Li et al.
Summary: This paper investigates the issue of class imbalance in liver cancer prediction and introduces two undersampling methods, which are applied to a five-year liver cancer prediction project in China. Experimental results demonstrate the superiority of our methods over existing approaches, providing a feasible and practical solution to address imbalanced data in cancer prediction.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Hardware & Architecture
Egawati Panjei et al.
Summary: This paper presents a survey on outlier explanations, focusing on mining meaningful knowledge from anomalous data to provide explanations. It discusses the challenges in generating different types of outlier explanations and reviews existing techniques addressing those challenges. The paper also examines the applications and evaluation methods of outlier explanations, and presents potential future research directions.
Article
Computer Science, Artificial Intelligence
Zhong-Yang Xiong et al.
Summary: Outlier detection is crucial in data mining tasks, with commonly used algorithms classified as distance-based and density-based, each having their own drawbacks. This paper proposes an outlier detection method based on the average divergence difference of data objects, which improves the accuracy of local outlier detection by developing new measures on the skewed distribution characteristics of data objects and their neighbors.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Fengbin Zhang et al.
Summary: The paper proposes a novel anomaly detection method that learns the latent representation of nodes using structure and attribute autoencoders, while detecting abnormal nodes through a dual-hypersphere learning mechanism. Experimental results demonstrate the superior performance of the proposed method in anomaly detection on attributed networks compared to existing techniques.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Yang Gao et al.
Summary: This paper addresses the challenges of detecting instances from emerging classes over a non-stationary data stream during data classification. It proposes a practical semi-supervised emerging class detection framework that incorporates a mutual graph clustering mechanism, performs online normalization, and uses only a small amount of true labels for training.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Angela Fernandez et al.
Summary: Outlier detection is a crucial task in machine learning, and removing outliers is important for accurate predictions. This paper proposes a new supervised outlier estimator by combining an outlier detector with a supervised model, allowing for optimal selection of hyperparameters. Experimental results demonstrate the effectiveness of this approach in determining detector hyperparameters and analyzing the performance of different outlier detectors.
Review
Computer Science, Information Systems
Imen Souiden et al.
Summary: The rapid evolution of technology has generated high-dimensional data streams in various fields, posing challenges for outlier detection. This study aims to examine existing approaches, identify comparison criteria, and highlight the challenges and research directions associated with this problem.
COMPUTER SCIENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Ankit Kumar et al.
Summary: This paper investigates a novel method for detecting cluster outliers in a multidimensional dataset, and achieves improvement. The proposed algorithm can identify groups and outliers in the dataset, and improve the results. Compared to existing algorithms, the proposed algorithm improves both in terms of accurate average value and recall rate.
ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS
(2022)
Article
Mathematical & Computational Biology
Shomona Gracia Jacob et al.
Summary: This research proposes a new graph-based methodology for detecting outliers in gene-protein mapping related to neuro-degenerative disorders. The study reveals protein physicochemical properties and their corresponding genes, and introduces a simple graphical approach to visualize this mapping. The proposed methodology can be extended to detect noisy outlier data from other biological and clinical datasets.
NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Bahador Rashidi et al.
Summary: This paper introduces a new deep learning approach, which combines correlative stacked auto-encoder (C-SAE) and correlative deep neural networks (C-DNN), for output-related anomaly detection in non-stationary processes. The proposed method can be applied to both linear and non-linear processes, and it incorporates non-linear correlation analysis into the structure to improve performance.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Heejeong Choi et al.
Summary: In this paper, a real-time explainable anomaly detection framework for predictive maintenance in a manufacturing system is proposed, which can identify abnormal signs early and provide explanations for each detected shutdown.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Zengri Zeng et al.
Summary: This paper proposes a detection system based on causal deep learning to improve the stability and generalization of network intrusion detection systems. By optimizing causal weights and removing correlations between weights, the system achieves good stability and can handle different network environments.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2022)
Article
Engineering, Electrical & Electronic
Pratibha Kumari et al.
Summary: This article introduces a method for considering concept drift in audio anomaly detection. The authors propose using dynamic Huffman coding instead of adaptive Gaussian mixture modeling to adapt to changes in audio. By merging close clusters instead of replacing rare clusters, the authors successfully increase the area under the curve.
IEEE SENSORS JOURNAL
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Mohamed Jaward Bah et al.
Summary: Outlier detection is a crucial task in data mining, but it is challenging to develop effective methods when labeled data is insufficient. This paper proposes a new solution that uses generative adversarial active learning to generate diverse, informative, and representative outlier samples, resulting in improved outlier detection accuracy.
2022 IEEE 34TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Anne Marthe Sophie Ngo Bibinbe et al.
Summary: In this study, a novel algorithm called DragStream is proposed for detecting subsequence anomalies and concept drifts in univariate data streams. The method performs competitively in terms of performance and has linear time and memory complexity.
2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Sinong Zhao et al.
Summary: This paper proposes a Multi-Subspace Deviation Network (MSDN) framework that combines feature learning with anomaly score learning, achieving higher accuracy in anomaly detection.
2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)
(2022)
Proceedings Paper
Engineering, Marine
Zelalem Engida et al.
Summary: Ocean Networks Canada operates an ocean observatory with more than 30 hydrophones that generate over 7TB of audio data per year. To quickly analyze this data, power spectral density plots are used, but manual inspection of thousands of images is time-consuming. To automate the process, a machine learning model was trained to identify anomalous spectrograms, achieving a 93% accuracy rate.
2022 OCEANS HAMPTON ROADS
(2022)
Article
Computer Science, Artificial Intelligence
Longbing Cao
Summary: This article discusses the ubiquitous assumption of independent and identically distributed (IID) in science, technology, engineering, and their applications, and highlights its limitations and gaps. It emphasizes the need for non-IID thinking approach in solving real-world problems, and discusses the concepts, challenges, and prospects of non-IID thinking, informatics, and learning.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Yu Wang et al.
Summary: An unsupervised outlier detection method for datasets with mixed-valued attributes based on an adaptive k-NN global network is proposed in this study. By introducing an adaptive search algorithm and a Heterogeneous Euclidean-Overlap Metric for distance measurement, as well as using transition probabilities to limit behaviors of random walkers, the method effectively detects outliers in the dataset.
Article
Computer Science, Artificial Intelligence
Sebastian Buschjaeger et al.
Summary: Isolation forest is a popular outlier detection algorithm that isolates outlier observations from regular observations by building multiple random isolation trees. This paper presents a theoretical framework that explains the good practical performance of isolation-based approaches and derives a generalized isolation forest for outlier scoring.
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS
(2022)
Article
Computer Science, Artificial Intelligence
Yue He et al.
Summary: This paper introduces a new dataset NICO for Non-I.I.D. image classification, which aims to address the common issue of Non-IIDness in practice. Experimental results show that NICO can support training ConvNet models effectively, and a batch balancing module can improve ConvNet performance in Non-I.I.D. settings.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Interdisciplinary Applications
Goncalo Jesus et al.
Summary: In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We suggest using machine learning techniques to model each sensor behavior and exploit correlated data provided by other related sensors to obtain accurate estimations of the observed environment parameters, improving the overall data quality. Our methodology not only allows to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also quantifies the quality of each measurement, relevant from a dependability perspective.
ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chayut Wiwatcharakoses et al.
Summary: Continual learning algorithms like GSOINN+ can adapt to changing data distributions and new tasks without forgetting previously learned knowledge. GSOINN+ uses a weighted nearest-neighbor rule for classification and can learn new tasks incrementally without the need for specifying them beforehand or rehearsing stored training sets.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Edouard Fouche et al.
Summary: This paper introduces a new method SGMRD that efficiently monitors interesting subspaces in high-dimensional data streams, leading to improved results in downstream data mining tasks.
INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Guansong Pang et al.
Summary: This study introduces a novel outlier detection framework to identify outliers in categorical data by capturing non-IID outlier factors. The graph representation and mining approach is employed to well capture the rich non-IID characteristics.
DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Computer Science, Artificial Intelligence
Guancen Lin et al.
Summary: Stock time series forecasting is a crucial purpose for academic researchers. In this paper, a modified modeling procedure combining EEMD and MKNN-TSPI methods is proposed to enhance prediction accuracy. Experimental results show that the proposed EEMD-MKNN-TSPI model outperforms other models, indicating its effectiveness in stock market prediction.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Review
Engineering, Electrical & Electronic
Lukas Ruff et al.
Summary: Deep learning approaches have significantly enhanced anomaly detection performance on complex data sets, sparking a renewed interest in the field. Various new methods have been introduced, including those based on generative models, one-class classification, and reconstruction. It is crucial to bring these methods together and explore the underlying principles and connections between classic and novel approaches for future research and development in anomaly detection.
PROCEEDINGS OF THE IEEE
(2021)
Proceedings Paper
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Mahboobeh Riahi-Madvar et al.
Summary: The research proposes a method of subspace outlier detection in high-dimensional data using an ensemble of PCA-based subspaces, which combines the results of multiple subspaces to effectively address outlier detection issues in high-dimensional data.
2021 26TH INTERNATIONAL COMPUTER CONFERENCE, COMPUTER SOCIETY OF IRAN (CSICC)
(2021)
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BMC MEDICAL INFORMATICS AND DECISION MAKING
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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
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