Outlier Analysis

PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling

Yan, X., Zheng, C., Li, Z., Wang, S., & Cui, S. R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00563 · Cited by 709

Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

Choi, K., Yi, J., Park, C., & Yoon, S. (2021) · IEEE Access · doi:10.1109/access.2021.3107975 · Cited by 660

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions

Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3159580 · Cited by 516

A comprehensive survey of anomaly detection techniques for high dimensional big data

Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 488

PointCleanNet : Learning to Denoise and Remove Outliers from Dense Point Clouds

Rakotosaona, M., Barbera, V. L., Guerrero, P., Mitra, N. J., & Ovsjanikov, M. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13753 · Cited by 381

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

Inoue, J., Yamagata, Y., Chen, Y., POSKITT, C. M., & Sun, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.149 · Cited by 372

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

Alghushairy, O., Alsini, R. A. I., Soule, T., & Ma, X. (2020) · Big Data and Cognitive Computing · doi:10.3390/bdcc5010001 · Cited by 384

Novel Geometric Area Analysis Technique for Anomaly Detection Using Trapezoidal Area Estimation on Large-Scale Networks

Moustafa, N., Slay, J., & Creech, G. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2715166 · Cited by 320

A survey of density based clustering algorithms

Bhattacharjee, P., & Mitra, P. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-9059-3 · Cited by 273

Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Fahim, M., & Sillitti, A. (2019) · IEEE Access · doi:10.1109/access.2019.2921912 · Cited by 238

Anomaly Detection of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder

Li, L., Yan, J., Wang, H., & Jin, Y. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2980749 · Cited by 222

Interactive Anomaly Detection on Attributed Networks

Ding, K., Li, J., & Liu, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3290964 · Cited by 171

Grappling With the COVID-19 Health Crisis: Content Analysis of Communication Strategies and Their Effects on Public Engagement on Social Media

Ngai, C. S. B., Singh, R. G., Lu, W., & Koon, A. C. (2020) · Journal of Medical Internet Research · doi:10.2196/21360 · Cited by 143

A Survey on Explainable Anomaly Detection

Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139

Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization

Carcillo, F., Borgne, Y. L., Caelen, O., & Bontempi, G. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0116-z · Cited by 128

A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection

Sheynin, S., Benaim, S., & Wolf, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00838 · Cited by 111

Testing for outliers with conformal p-values

Bates, S., Candès, E. J., Lei, L., Romano, Y., & Sesia, M. (2023) · The Annals of Statistics · doi:10.1214/22-aos2244 · Cited by 112

Improving the Accuracy of Convolutional Neural Networks by Identifying and Removing Outlier Images in Datasets Using t-SNE

Perez, H., & Tah, J. H. M. (2020) · Mathematics · doi:10.3390/math8050662 · Cited by 103

MOCCA: Multilayer One-Class Classification for Anomaly Detection

Massoli, F. V., Falchi, F., Kantarcı, A., Aktı, Ş., Ekenel, H. K., & Amato, G. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3130074 · Cited by 90

A Deep Multi-View Framework for Anomaly Detection on Attributed Networks

Peng, Z., Luo, M., Li, J., Xue, L., & Zheng, Q. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3015098 · Cited by 85

Conformal Prediction for Time Series

Xu, C., & Xie, Y. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3272339 · Cited by 76

A survey on pre-processing techniques: Relevant issues in the context of environmental data mining

Gibert, K., Sànchez–Marrè, M., & Izquierdo, J. (2016) · AI Communications · doi:10.3233/aic-160710 · Cited by 77

Memorizing Structure-Texture Correspondence for Image Anomaly Detection

Zhou, K. Q., Li, J., Xiao, Y., Yang, J., Cheng, J. T., Liu, W., Luo, W., Liu, J., & Gao, S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3101403 · Cited by 78

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Serra, A., Fratello, M., Cattelani, L., Liampa, I., Melagraki, G., Kohonen, P. J., Nymark, P. E. H., Federico, A., Kinaret, P., Jagiełło, K., et al. (2020) · Nanomaterials · doi:10.3390/nano10040708 · Cited by 76

Smartphone-Based Adaptive Driving Maneuver Detection: A Large-Scale Evaluation Study

Castignani, G., Derrmann, T., Frank, R., & Engel, T. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2646760 · Cited by 67

On Using Classification Datasets to Evaluate Graph Outlier Detection: Peculiar Observations and New Insights

Zhao, L., & Akoglu, L. (2021) · Big Data · doi:10.1089/big.2021.0069 · Cited by 61

Anomaly Detection on Data Streams for Smart Agriculture

Moso, J. C., Cormier, S., de Runz, C., Fouchal, H., & Wandeto, J. (2021) · Agriculture · doi:10.3390/agriculture11111083 · Cited by 60

Outlier detection for wireless sensor networks using density‐based clustering approach

Abid, A., Kachouri, A., & Mahfoudhi, A. (2017) · IET Wireless Sensor Systems · doi:10.1049/iet-wss.2016.0044 · Cited by 57

Multidimensional Perspective of Firms’ IT Capability Between Digital Business Strategy and Firms’ Efficiency: A Case of Chinese SMEs

Wang, Z., Mahmood, M. R., Ullah, H., Hanif, I., Abbas, Q., & Mohsin, M. (2020) · SAGE Open · doi:10.1177/2158244020970564 · Cited by 65

DeepIntent

Xi, S., Yang, S., Xiao, X., Yao, Y., Xiong, Y., Xu, F., Wang, H., Gao, P., Liu, Z., Xu, F., et al. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3363193 · Cited by 58

Identifying Medicare Provider Fraud with Unsupervised Machine Learning

Bauder, R. A., da Rosa, R., & Khoshgoftaar, T. M. (2018) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2018.00051 · Cited by 54

Supervised outlier detection for classification and regression

Fernández, Á., Bella, J., & Dorronsoro, J. R. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.02.047 · Cited by 55

Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data

Yokkampon, U., Mowshowitz, A., Chumkamon, S., & Hayashi, E. (2022) · IEEE Access · doi:10.1109/access.2022.3178592 · Cited by 51

ECHAD: Embedding-Based Change Detection From Multivariate Time Series in Smart Grids

Ceci, M., Corizzo, R., Japkowicz, N., Mignone, P., & Pio, G. (2020) · IEEE Access · doi:10.1109/access.2020.3019095 · Cited by 48

Finding Suspicious Activities in Financial Transactions and Distributed Ledgers

Camino, R. D., State, R., Montero, L. P., & Valtchev, P. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.109 · Cited by 47

Detecting Malicious Model Updates from Federated Learning on Conditional Variational Autoencoder

Gu, Z., & Yang, Y. (2021) · 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00075 · Cited by 47

PointFilterNet: A Filtering Network for Point Cloud Denoising

Wang, X., Xiaopeng Fan, B., & Zhao, D. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207789 · Cited by 47

Log-Based Anomaly Detection of CPS Using a Statistical Method

Harada, Y., Yamagata, Y., Mizuno, O., & Choi, E.-H. (2017) · International Workshop on Empirical Software Engineering in Practice (IWESEP) · doi:10.1109/iwesep.2017.12 · Cited by 44

Deep Active Learning for Anomaly Detection

Pimentel, T., Monteiro, M., Veloso, A., & Ziviani, N. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206769 · Cited by 56

Transdisciplinary Foundations of Geospatial Data Science

Xie, Y., Eftelioglu, E., Ali, R. Y., Tang, X., Yan, L., Doshi, R., & Shekhar, S. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6120395 · Cited by 44

Detection of False Data Injection Attacks Using the Autoencoder Approach

Wang, C., Tindemans, S. H., Pan, K., & Pálenský, P. (2020) · International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) · doi:10.1109/pmaps47429.2020.9183526 · Cited by 44

Data stream classification with novel class detection: a review, comparison and challenges

Din, S. U., Shao, J., Kumar, J., Mawuli, C. B., Mahmud, S., Zhang, W., & Yang, Q. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01582-4 · Cited by 44

Building Resilience Through Foresight: The Case of Maritime Container Shipping Firms

Bathke, H., Münch, C., von der Gracht, H. A., & Hartmann, E. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2021.3137009 · Cited by 50

Improving Data Quality Through Deep Learning and Statistical Models

Dai, W., Yoshigoe, K., & Parsley, W. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-54978-1_66 · Cited by 47

EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data

Xu, K., Xia, M., Mu, X., Wang, Y., & Cao, N. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864825 · Cited by 47

MemStream: Memory-Based Streaming Anomaly Detection

Bhatia, S., Jain, A., Srivastava, S., Kawaguchi, K., & Hooi, B. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3512221 · Cited by 42

Randomized outlier detection with trees

Buschjäger, S., Honysz, P.-J., & Morik, K. J. (2020) · International Journal of Data Science and Analytics · doi:10.1007/s41060-020-00238-w · Cited by 43

Detecting Outlier Machine Instances Through Gaussian Mixture Variational Autoencoder With One Dimensional CNN

Su, Y., Zhao, Y., Sun, M., Zhang, S., Wen, X., Zhang, Y., Liu, X., Liu, X., Tang, J., Wu, W., et al. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3065073 · Cited by 44

A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims

Bauder, R. A., & Khoshgoftaar, T. M. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0063 · Cited by 41

Adaptive k-Nearest-Neighbor Method for Process Monitoring

Zhu, W., Sun, W. H., & Romagnoli, J. A. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b03771 · Cited by 39

A Bayesian Real-Time Electric Vehicle Charging Strategy for Mitigating Renewable Energy Fluctuations

Latifi, M., Khalili, A., Rastegarnia, A., & Sanei, S. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2866267 · Cited by 38

Anomaly Detection in Trajectory Data with Normalizing Flows

Dias, M. L. D., Mattos, C. L. C., da Silva, T. L. C., de Macêdo, J. A. F., & Silva, W. C. P. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206939 · Cited by 39

Traffic Data Recovery From Corrupted and Incomplete Observations via Spatial-Temporal TRPCA

Feng, X., Zhang, H., Wang, C., & Zheng, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3151925 · Cited by 38

Multivariate outlier detection in medicare claims payments applying probabilistic programming methods

Bauder, R. A., & Khoshgoftaar, T. M. (2017) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-017-0172-1 · Cited by 37

Combining GANs and AutoEncoders for efficient anomaly detection

Carrara, F., Amato, G. M., Brombin, L., Falchi, F., & Gennaro, C. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412253 · Cited by 15

Negative Selection Algorithm Research and Applications in the Last Decade: A Review

Gupta, K. D., & Dasgupta, D. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3114661 · Cited by 37

Robust parameter estimation for dynamical systems from outlier-corrupted data

Maier, C., Loos, C., & Hasenauer, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw703 · Cited by 34

Outlier Detection for Improved Data Quality and Diversity in Dialog Systems

Larson, S., Mahendran, A., Lee, A. S., Kummerfeld, J. K., Hill, P., Laurenzano, M. A., Hauswald, J., Tang, L., & Mars, J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1051 · Cited by 36

Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Barbariol, T., Feltresi, E., & Susto, G. A. (2020) · Energies · doi:10.3390/en13123136 · Cited by 33

The causes of firm performance variation in the Czech food processing industry in the context of the outlier effect

Blažková, I., & Dvouletý, O. (2018) · Management Research Review · doi:10.1108/mrr-05-2017-0142 · Cited by 32

Tensor Graph Convolutional Networks for Multi-Relational and Robust Learning

Ioannidis, V. N., Marques, A. G., & Giannakis, G. B. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3028495 · Cited by 33

Graph Attention Network and Informer for Multivariate Time Series Anomaly Detection

Zhao, M., Peng, H., Li, L., & Ren, Y. (2024) · Sensors · doi:10.3390/s24051522 · Cited by 37

Anomaly Detection with Deep Graph Autoencoders on Attributed Networks

Zhu, D., Ma, Y., & Liu, Y. (2020) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc50000.2020.9219684 · Cited by 30

Environmental microorganism classification using optimized deep learning model

Liang, C., Lai, C.-C., Wang, S., & Lin, Y. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-13010-9 · Cited by 31

A Hybrid Outlier Detection Method for Health Care Big Data

Yan, K., You, X., Ji, X., Yin, G., & Yang, F. (2016) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.34 · Cited by 30

Statistical Analysis and Prediction of Parking Behavior

Feng, N., Zhang, F., Lin, J., Zhai, J., & Du, X. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30709-7_8 · Cited by 28

Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider

Pol, A. A., Cerminara, G., Germain‐Renaud, C., Maurizio Pierini, B., & Seth, A. (2019) · Computing and Software for Big Science · doi:10.1007/s41781-018-0020-1 · Cited by 29

cleanTS: Automated (AutoML) tool to clean univariate time series at microscales

Shende, M. K., Feijóo, A., & Bokde, N. D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.05.057 · Cited by 26

Data Observability for High-Throughput Payments Pipelines: SLA Design, Anomaly Budgets, and Sequential Probability Ratio Tests for Early Incident Detection

Amebleh, J., & Omachi, A. (2022) · International Journal of Scientific Research in Science Engineering and Technology · doi:10.32628/ijsrset221658 · Cited by 26

Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements

Nikula, R.-P., Ruusunen, M., Keski-Rahkonen, J., Saarinen, L., & Fagerholm, F. (2021) · Machines · doi:10.3390/machines9020039 · Cited by 43

A Global Probabilistic Prediction of Cold Seeps and Associated SEAfloor FLuid Expulsion Anomalies (SEAFLEAs)

Phrampus, B. J., Lee, T. R., & Wood, W. T. (2019) · Geochemistry Geophysics Geosystems · doi:10.1029/2019gc008747 · Cited by 25

Fast and Exact Outlier Detection in Metric Spaces

Amagata, D., Onizuka, M., & Hara, T. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3452782 · Cited by 24

Enhancing Unsupervised Anomaly Detection With Score-Guided Network

Huang, Z., Zhang, B., Hu, G., Li, L., Xu, Y., & Jin, Y. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3281501 · Cited by 25

Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

Kotlar, M., Punt, M., Radivojević, Z., Cvetanović, M., & Milutinović, V. M. (2021) · IEEE Access · doi:10.1109/access.2021.3090936 · Cited by 26

Learning to Detect Anomalous Wireless Links in IoT Networks

Cerar, G., Yetgin, H., Bertalanič, B., & Fortuna, C. (2020) · IEEE Access · doi:10.1109/access.2020.3039333 · Cited by 23

Exploration of intelligent housing price forecasting based on the anchoring effect

Song, Y. L., & Ma, X. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08823-3 · Cited by 23

An efficient data aggregation and outlier detection scheme based on radial basis function neural network for WSN

Ullah, I., Youn, H. Y., & Han, Y. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02703-7 · Cited by 23

Hybrid graph transformer networks for multivariate time series anomaly detection

Gao, R., He, W. P., Yan, L., Liu, D., Yu, Y., & Ye, Z. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05503-w · Cited by 23

An independent central point OPTICS clustering algorithm for semi-supervised outlier detection of continuous glucose measurements

Zhao, Y., Li, H., Yu, X., Ma, N., Yang, T., & Zhou, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103196 · Cited by 23

Entropy Isolation Forest Based on Dimension Entropy for Anomaly Detection

Liao, L., & Luo, B. (2019) · Communications in computer and information science · doi:10.1007/978-981-13-6473-0_32 · Cited by 22

Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs

Cakmak, E., Schlegel, U., Jäckle, D., Keim, D. A., & Schreck, T. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030398 · Cited by 21

A novel approach for the detection of anomalous energy consumption patterns in industrial cyber‐physical systems

Mendia, I., Gil-López, S., Grau, I., & Ser, J. D. (2022) · Expert Systems · doi:10.1111/exsy.12959 · Cited by 21

Brain Lesion Detection Using A Robust Variational Autoencoder and Transfer Learning

Akrami, H., Joshi, A. A., Li, J., Aydöre, S., & Leahy, R. M. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098405 · Cited by 21

A hybrid evolutionary approach for identifying spam websites for search engine marketing

Aswani, R., Ghrera, S. P., Chandra, S., & Kar, A. K. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00461-1 · Cited by 21

Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy

Wang, J., Wang, X., & Yu, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-85308-4 · Cited by 25

Anomaly Detection for Primary Distribution System Measurements using Principal Component Analysis

Imayakumar, A., Dubey, A., & Bose, A. (2020) · IEEE Texas Power and Energy Conference (TPEC) · doi:10.1109/tpec48276.2020.9042509 · Cited by 20

Anomaly detection in the time-series data of industrial plants using neural network architectures

Dix, M., Chouhan, A. P. S., Ganguly, S., Pradhan, S., Saraswat, D., Agrawal, S., & Prabhune, A. (2021) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice52369.2021.00035 · Cited by 20

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

Serrano‐Guerrero, X., Escrivá-Escrivà, G., Romero, S. F. L., & Clairand, J. (2020) · Energies · doi:10.3390/en13051046 · Cited by 20

Clustering-Based Deep Autoencoders for Network Anomaly Detection

Nguyen, V. Q., Nguyen, V. H., Le‐Khac, N., & Cao, V. L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-63924-2_17 · Cited by 20

Detecting the Onset of Machine Failure Using Anomaly Detection Methods

Riazi, M., Zai͏̈ane, O. R., Takeuchi, T., Maltais, A., Günther, J., & Lipsett, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27520-4_1 · Cited by 18

Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions

Charte, D., Charte, F., & Herrera, F. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3127698 · Cited by 21

Anomaly Detection in Maritime Domain based on Spatio-Temporal Analysis of AIS Data Using Graph Neural Networks

Eljabu, L., Etemad, M., & Matwin, S. (2021) · International Conference on Vision, Image and Signal Processing (ICVISP) · doi:10.1109/icvisp54630.2021.00033 · Cited by 19

Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

Das, M., & Ghosh, S. K. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9349-0 · Cited by 19

Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach

Niu, G., Yang, P., Zheng, Y., Cai, X., & Qin, H. (2021) · Water Resources Research · doi:10.1029/2020wr029121 · Cited by 19

Outlier Detection on Mixed-Type Data: An Energy-Based Approach

Do, K., Tran, T., Phung, D., & Venkatesh, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49586-6_8 · Cited by 20

Range Based Confusion Matrix for Imbalanced Time Series Classification

Zhou, X., & Valle, A. D. (2020) · International Conference on Data Science and Machine Learning Applications (CDMA) · doi:10.1109/cdma47397.2020.00006 · Cited by 18

DeepAD: A Joint Embedding Approach for Anomaly Detection on Attributed Networks

Zhu, D., Ma, Y., & Liu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50417-5_22 · Cited by 18

Data Reconstruction in Heterogeneous Environmental Wireless Sensor Networks Using Robust Tensor Principal Component Analysis

Rajesh, G., & Chaturvedi, A. (2021) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2021.3105795 · Cited by 17

Unsupervised Adaptive Fleet Battery Pack Fault Detection With Concept Drift Under Evolving Environment

Peng, X., Duan, S., Sankavaram, C., & Jin, X. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3363002 · Cited by 17

Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality

Shetta, O., & Niranjan, M. (2020) · Royal Society Open Science · doi:10.1098/rsos.190714 · Cited by 17

Introduction

Hawkins, D. M. (1980) · Springer eBooks · doi:10.1007/978-94-015-3994-4_1 · Cited by 28

An Introduction to Outlier Analysis

Aggarwal, C. C. (2016) · Springer eBooks · doi:10.1007/978-3-319-47578-3_1 · Cited by 296

Novelty Detection

Sanguinetti, G., & Hawkins, D. (2009)

Outlier Detection, Influential Observations and Robust Estimation of Principal Components

Jolliffe, I. T. (1986) · Springer series in statistics · doi:10.1007/978-1-4757-1904-8_10 · Cited by 1

Anomaly Detection

Cleophas, T. J., & Zwinderman, A. H. (2013) · Springer eBooks · doi:10.1007/978-94-007-6886-4_10