A review of novelty detection

UCI Machine Learning Repository

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Model selection and application to high-dimensional count data clustering

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Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting

Lučić, A., Haned, H., & de Rijke, M. (2019) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1908.00085 · Cited by 13

Abstraction-Based Outlier Detection for Image Data

Yakovlev, K., Bekkouch, I. E. I., Khan, A., & Khattak, A. M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55180-3_40 · Cited by 13

Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine

Tonin, F., Pandey, A., Patrinos, P., & Suykens, J. A. K. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533706 · Cited by 13

Local and Global Outlier Detection Algorithms in Unsupervised Approach: A Review

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Sequential Attack Detection in Recommender Systems

Aktukmak, M., Yılmaz, Y., & Uysal, I. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3076295 · Cited by 13

CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System

Qin, H., Zhan, X., & Zheng, Y. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3154166 · Cited by 13

Model-free detection of unique events in time series

Benkő, Z., Bábel, T., & Somogyvári, Z. (2022) · Scientific Reports · doi:10.1038/s41598-021-03526-y · Cited by 13

Relative density degree induced boundary detection for one-class SVM

Zhu, F., Yang, J. J., Xu, S., Gao, C., Ye, N., & Yin, T. (2015) · Soft Computing · doi:10.1007/s00500-015-1757-7 · Cited by 13

Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor

Wielgosz, M., Skoczeń, A., & Matteis, E. D. (2018) · Sensors · doi:10.3390/s18113933 · Cited by 14

Stream Data Preprocessing: Outlier Detection Based on the Chebyshev Inequality with Applications

Shevlyakov, G. L., & Kan, M. (2020) · Conference of Open Innovations Association (FRUCT) · doi:10.23919/fruct48808.2020.9087459 · Cited by 13

Memory-Augmented Anomaly Generative Adversarial Network for Retinal OCT Images Screening

Zhang, C., Wang, Y., Zhao, X., Guo, Y., Xie, G., Lv, C., & Lv, B. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098717 · Cited by 13

IT-Sicherheit in digitalen Stationen: Cyber-physische Systemmodellierung, -bewertung und -analyse

Rösch, D., Kummerow, A., Ruhe, S., Schäfer, K., Monsalve, C., & Nicolai, S. (2020) · at - Automatisierungstechnik · doi:10.1515/auto-2020-0077 · Cited by 13

Landing AI on Networks: An Equipment Vendor Viewpoint on Autonomous Driving Networks

Rossi, D., & Zhang, L. (2022) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2022.3169988 · Cited by 16

Toward a self-adaptive digital twin based Active learning method: an application to the lumber industry

Chabanet, S., El-Haouzi, H. B., & Thomas, P. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.223 · Cited by 13

End-of-line fault detection for combustion engines using one-class classification

Leitner, L., Lagrange, A., & Endisch, C. (2016) · IEEE International Conference on Advanced Intelligent Mechatronics (AIM) · doi:10.1109/aim.2016.7576768 · Cited by 13

A Variational Dirichlet Framework for Out-of-Distribution DetectionPreprint

Chen, W., Shen, Y., Jin, H., & Wang, W. Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.07308 · Cited by 25

Multivariate Triangular Quantile Maps for Novelty Detection

Wang, J., Sun, S., & Yu, Y. (2019) · UWSpace (University of Waterloo) · Cited by 13

The Hidden Uncertainty in a Neural Networks ActivationsPreprint

Postels, J., Blum, H., Strümpler, Y., Cadena, C., Siegwart, R., Gool, L. V., & Tombari, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03082 · Cited by 46

Botnet detection in internet of things using stacked ensemble learning model

Ali, M., Mushtaq, M. F., Akram, U., Aray, D. G., Vergara, M. M., Karamti, H., & Ashraf, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-02008-9 · Cited by 12

Novelty detection for breast cancer image classification

Cichosz, P., Jagodziński, D., Matysiewicz, M., Neumann, Ł., Nowak, R. M., Okuniewski, R., & Oleszkiewicz, W. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2249183 · Cited by 12

Selective Prediction-Set Models with Coverage Rate Guarantees

Feng, J., Sondhi, A., Perry, J., & Simon, N. (2021) · Biometrics · doi:10.1111/biom.13612 · Cited by 31

End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection

Huang, T., Wang, D., Fang, Y., & Chen, Z. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/290 · Cited by 12

Stochastic conformal anomaly detection and resolution for air traffic control

Choi, H.-C., Deng, C., Park, H., & Hwang, I. (2023) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2023.104259 · Cited by 13

Review on novelty detection in the non-stationary environment

Agrahari, S., Srivastava, S., & Singh, A. K. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02018-x · Cited by 12

Incremental novelty detection and fault identification scheme applied to a kinematic chain under non-stationary operation

Carino, J. A., Delgado-Prieto, M., Zurita, D., Picot, A., Ortega, J. A., & Romero-Troncoso, R. D. J. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.07.025 · Cited by 12

Incremental rare pattern based approach for identifying outliers in medical data

Borah, A., & Nath, B. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105824 · Cited by 12

Unsupervised Anomaly Detection and Root Cause Analysis in Mobile Networks

Kim, C., Mendiratta, V., & Thottan, M. (2020) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets48256.2020.9027328 · Cited by 14

Detecting Pattern Anomalies in Hydrological Time Series with Weighted Probabilistic Suffix Trees

Yu, Y., Wan, D., Zhao, Q., & Liu, H. (2020) · Water · doi:10.3390/w12051464 · Cited by 12

On novelty detection for multi-class classification using non-linear metric learning

Silva, S. R., Vieira, T. M. D. A., Martínez, D., & Paiva, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114193 · Cited by 12

Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networks

Hashemi, V., Křetínský, J., Mohr, S., & Seferis, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88494-9_14 · Cited by 12

Subset Multivariate Collective and Point Anomaly Detection

Fisch, A. T. M., Eckley, I. A., & Fearnhead, P. (2021) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2021.1987257 · Cited by 12

Unsupervised abnormal detection using VAE with memory

Xie, X., Li, X., Wang, B., Wan, T., Xu, L., & Li, H. (2022) · Soft Computing · doi:10.1007/s00500-022-07140-6 · Cited by 12

Time Series Data Mining for Sport Data: a Review

Komitova, R., Raabe, D., Rein, R., & Memmert, D. (2022) · Int. J. Comput. Sci. Sport · doi:10.2478/ijcss-2022-0008 · Cited by 13

Energy-Efficient Self-Supervised Technique to Identify Abnormal User Over 5G Network for E-Commerce

Haider, S. A., Rahman, M. Z. U., Gupta, S. K., Ataniyazov, J., Soomar, A. M., Gupta, B., Patni, J. C., & Chunduri, V. (2024) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2024.3355477 · Cited by 12

Human gait pattern changes detection system: A multimodal vision-based and novelty detection learning approach

Paulo, J., Asvadi, A., Peixoto, P., & Amorim, P. (2017) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2017.08.002 · Cited by 12

SECODA: Segmentation- and Combination-Based Detection of Anomalies

Foorthuis, R. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.35 · Cited by 12

An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data

Fathy, Y., Barnaghi, P. M., & Tafazolli, R. (2018) · IEEE Systems Journal · doi:10.1109/jsyst.2018.2876461 · Cited by 14

Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning

Sedlmeier, A., Gabor, T., Phan, T., & Belzner, L. (2019) · Digitale Welt · doi:10.1007/s42354-019-0238-z · Cited by 22

Acoustic diagnostics of electrical origin fault modes with readily available consumer‐grade sensors

Grebenik, J., Bingham, C. M., & Srivastava, S. (2019) · IET Electric Power Applications · doi:10.1049/iet-epa.2019.0232 · Cited by 12

Machine learning in industrial measurement technology for detection of known and unknown faults of equipment and sensors

Schneider, T., Klein, S., & Schütze, A. (2019) · tm - Technisches Messen · doi:10.1515/teme-2019-0086 · Cited by 12

Methods to quality assure, plot, summarize, interpolate, and extend groundwater-level information—examples for the Mississippi River Valley alluvial aquifer

Asquith, W. H., Seanor, R. C., McGuire, V. L., & Kress, W. H. (2020) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2020.104758 · Cited by 12

A Novel Framework for Synchrophasor Based Online Recognition and Efficient Post-Mortem Analysis of Disturbances in Power Systems

Kummerow, A., Monsalve, C., Brosinsky, C., Nicolai, S., & Westermann, D. (2020) · Applied Sciences · doi:10.3390/app10155209 · Cited by 14

Variational Autoencoder Based Fault Detection and Location Method for Power Distribution Network

Wang, X., Cui, P. Y., Du, Y., & Yang, Y. (2020) · International Conference on Condition Monitoring and Diagnosis (CMD) · doi:10.1109/cmd48350.2020.9287286 · Cited by 12

Data-Driven Predictive Maintenance in Evolving Environments: A Comparison Between Machine Learning and Deep Learning for Novelty Detection

Buono, F. D., Calabrese, F., Baraldi, A., Paganelli, M., & Regattieri, A. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-6128-0_11 · Cited by 12

Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps

Yang, L., & Rossi, D. (2021) · IEEE Network · doi:10.1109/mnet.001.2100227 · Cited by 12

From Novelty Detection to a Genetic Algorithm Optimized Classification for the Diagnosis of a SCADA-Equipped Complex Machine

Viale, L., Daga, A. P., Fasana, A., & Garibaldi, L. (2022) · Machines · doi:10.3390/machines10040270 · Cited by 12

A statistical approach to combining multisource information in one‐class classifiers

Simonson, K. M., West, R. D., Hansen, R. L., LaBruyere, T. E., & Benthem, M. H. V. (2017) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11342 · Cited by 14

This is not what I imagined: Error Detection for Semantic Segmentation through Visual DissimilarityPreprint

Haldimann, D., Blum, H., Siegwart, R., & Cadena, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.00676 · Cited by 15

A review and systematic guide to counteracting medical data scarcity for AI applications

Gröger, F., Amruthalingam, L., Lionetti, S., Navarini, A. A., Ille, F., & Pouly, M. (2025) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2025.100220 · Cited by 13

Dynamic Construction of Outlier Detector Ensembles With Bisecting K-Means Clustering

Koko, R. R. Z., Yassine, I. A., Wahed, M. A., Madete, J., & Rushdi, M. A. (2023) · IEEE Access · doi:10.1109/access.2023.3252004 · Cited by 11

DMVSVDD: Multi-View Data Novelty Detection with Deep Autoencoding Support Vector Data Description

Chen, Z., Zhao, K., Sun, S., Li, J., Wang, S., & Sun, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122443 · Cited by 12

Multivariable correlation feature network construction and health condition assessment for unlabeled single-sample data

Xu, D., Xiao, X., & Zhang, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108220 · Cited by 12

Determination of quality classes for material extrusion additive manufacturing using image processing

Oleff, A., Küster, B., & Overmeyer, L. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13269-5 · Cited by 11

Unsupervised Homography Estimation With Pixel-Level SVDD

Zhou, Z., Zhu, Q. X., Feng, M., Wang, Y., Luo, J., Miao, Z., Chen, L., & Mo, Y. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3394899 · Cited by 11

Managing the unknown in machine learning: Definitions, related areas, recent advances, and prospects

Barcina-Blanco, M., Lobo, J. L., Bringas, P. G., & Ser, J. D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128073 · Cited by 23

Vision Transformer–Based Anomaly Detection Method for Offshore Platform Monitoring Data

Zhu, Q., Wu, Q., Yue, Y., Bao, Y., Zhang, T., Wang, X., Jiang, Z., & Chen, H. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/1887212 · Cited by 11

A black-box assessment of authentication and reliability in consumer IoT devices

Lazzaro, S., Angelis, V. D., Mandalari, A. M., & Buccafurri, F. (2025) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2025.102045 · Cited by 13

Safety Monitoring of Machine Learning Perception Functions: A Survey

Ferreira, R., Guerin, J. M., Delmas, K., Guiochet, J., & Waeselynck, H. (2025) · Computational Intelligence · doi:10.1111/coin.70032 · Cited by 23

Enhancing robustness and sparsity: Least squares one-class support vector machine

Kumari, A., & Tanveer, M. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111691 · Cited by 11

Model-free novelty-based diffusion MRI

Golkov, V., Sprenger, T., Sperl, J. I., Menzel, M. I., Czisch, M., Sämann, P. G., & Cremers, D. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493489 · Cited by 11

A null space based one class kernel Fisher discriminant

Dufrenois, F., & Noyer, J.-C. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727608 · Cited by 11

Identifying and responding to outlier demand in revenue management

Rennie, N., Cleophas, C., Sykulski, A. M., & Dost, F. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.01.002 · Cited by 11

Data Selection in Neural Networks

Ferreira, J. O., Mendonça, M. O. K., & Diniz, P. S. R. (2021) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2021.3106197 · Cited by 11

Estimation of fault probability in medium voltage feeders through calibration techniques in classification models

Santis, E. D., Arnò, F., & Rizzi, A. (2022) · Soft Computing · doi:10.1007/s00500-022-07194-6 · Cited by 11

Reference Density Database for 20 Aqueous Alkali Halide Solutions

Yang, F., Qu, J., Kontogeorgis, G. M., & de Hemptinne, J.-C. (2022) · Journal of Physical and Chemical Reference Data · doi:10.1063/5.0124173 · Cited by 11

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Li, J., Wang, X., Zhao, H., Wang, S., & Zhong, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25563 · Cited by 11

Automatic Generation Of Personalised Alert Thresholds For Patients With Copd

Farmer, A. A., Gibson, O., Rutter, H., Shah, S. A., Tarassenko, L., & Velardo, C. (2014) · INFM-OAR (INFN Catania) · doi:10.5281/zenodo.43966 · Cited by 11

Abnormality Detecting Deep Belief Network

Sharma, M. K., Sheet, D., & Biswas, P. K. (2016) · International Conference on Advances in Information Communication Technology & Computing · doi:10.1145/2979779.2979790 · Cited by 11

Siamese neural network for intelligent information security control in multi-robot systems

Utkin, L. V., Zaborovsky, V. S., & Popov, S. G. (2017) · Automatic Control and Computer Sciences · doi:10.3103/s0146411617080235 · Cited by 11

Road User Abnormal Trajectory Detection Using a Deep Autoencoder

Roy, P. R., & Bilodeau, G.-A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03801-4_65 · Cited by 11

Osmotic Collaborative Computing for Machine Learning and Cybersecurity Applications in Industrial IoT Networks and Cyber Physical Systems with Gaussian Mixture Models

Oyekanlu, E. A. (2018) · IEEE International Conference on Collaboration and Internet Computing (CIC) · doi:10.1109/cic.2018.00051 · Cited by 11

The use of support vectors from support vector machines for hydrometeorologic monitoring network analyses

Asquith, W. H. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.124522 · Cited by 11

A Comparison of GANs-Based Approaches for Combustor System Fault Detection

Xu, R., & Yan, W. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207487 · Cited by 12

Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning

Cho, P., Wood, A., Mahalingam, K., & Eyink, K. G. (2021) · Mathematics · doi:10.3390/math9111209 · Cited by 11

AEKOC+: Kernel Ridge Regression-Based Auto-Encoder for One-Class Classification Using Privileged Information

Gautam, C., Tiwari, A., & Tanveer, M. (2020) · Cognitive Computation · doi:10.1007/s12559-019-09705-4 · Cited by 11

Enabling Situational Awareness via Augmented Reality of Autonomous Robot-Based Environmental Change Detection

Reardon, C., Gregory, J. M., Nieto-Granda, C., & Rogers, J. G. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-49695-1_41 · Cited by 11

Extreme Value Theory for Open Set Classification -- GPD and GEV ClassifiersPreprint

Vignotto, E., & Engelke, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.09902 · Cited by 11

Selective prediction-set models with coverage guaranteesPreprint

Feng, J., Sondhi, A., Perry, J., & Simon, N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05473 · Cited by 11

Artificial Intelligence—Blockchain Enabled Technology for Internet of Things

Kumari, S., & Muthulakshmi, P. (2023) · Wiley eBooks · doi:10.1002/9781394213726.ch18 · Cited by 12

Concept drift detection in image data stream: a survey on current literature, limitations and future directions

Tran, Q.-T., Le‐Khac, N., & Bertolotto, M. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11428-y · Cited by 12

Physics-constrained digital twin framework for deformation analysis and safety assessment of high earth-rock dams

Huang, Z., Ma, G., Ai, Z., Wang, J., Chang, X., & Zhou, W. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112348 · Cited by 10

FSA-IDS: A Flow-based Self-Active Intrusion Detection System

Hajizadeh, M., Barua, S., & Golchin, P. (2023) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms56928.2023.10154343 · Cited by 10

Augmented Reality Visualization of Autonomous Mobile Robot Change Detection in Uninstrumented Environments

Reardon, C., Gregory, J. M., Haring, K. S., Dossett, B., Miller, O., & Inyang, A. B. (2023) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3611654 · Cited by 10

Clustering-Based Granular Representation of Time Series With Application to Collective Anomaly Detection

Shi, W. H., Karastoyanova, D., Ma, Y., Huang, Y., & Zhang, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3325521 · Cited by 10

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

Zhang, X., Li, L., & Qu, G. (2024) · Sensors · doi:10.3390/s24020505 · Cited by 10

Ensemble Uncertainty Guided Road Scene Anomaly Detection: A Simple Meta-Learning Approach

Liu, Y., Wei, X., Lasang, P., Pranata, S., Subramanian, K., & Seow, H. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3365609 · Cited by 10

Efficacious application of data-driven machine learning models for predicting and optimizing the flexural tensile strength of fiber-reinforced concrete

Abbas, Y. M., & Khan, M. I. (2024) · Structures · doi:10.1016/j.istruc.2024.106574 · Cited by 10

Unsupervised diffusion based anomaly detection for time series

Zuo, H., Zhu, A., Zhu, Y., Liao, Y., Li, S., & Chen, Y. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05341-0 · Cited by 11

Hard-Normal Example-Aware Template Mutual Matching for Industrial Anomaly Detection

Chen, Z., Xie, X., Yang, L., & Lai, J. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02323-0 · Cited by 12

Aligning, Autoencoding and Prompting Large Language Models for Novel Disease Reporting

Liu, F., Wu, X., Huang, J., Yang, B., Branson, K., Schwab, P., Clifton, L., Zhang, P., Luo, J., Zheng, Y., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3534586 · Cited by 13

A Multi-Scale Self-Supervision Approach for Bearing Anomaly Detection Using Sensor Data Under Multiple Operating Conditions

Dai, Z., Jiang, L., Li, F., & Chen, Y. (2025) · Sensors · doi:10.3390/s25041185 · Cited by 10

Application of Uncertainty to Out-of-Distribution Detection for Autonomous Driving Perception Safety

Wang, K., Ma, Q., Shen, C., & Lǚ, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3557996 · Cited by 13

Detection and classification of anomalies in oil well production using Open-World Learning

Lopes, L. G. O., Vieira, T. M. D. A., Aranha, P. E., de Lima, E. T., & Lira, W. W. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111514 · Cited by 10

Extreme learning machine based novelty detection for incremental semi-supervised learning

Al-Behadili, H., Grumpe, A., Dopp, C., & Wöhler, C. (2015) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip.2015.7414771 · Cited by 10

Improved prediction of missing protein interactome links via anomaly detection

Singh, K. V., & Vig, L. (2017) · Applied Network Science · doi:10.1007/s41109-017-0022-7 · Cited by 11

A Temporal Dependency Based Multi-modal Active Learning Approach for Audiovisual Event Detection

Thiam, P., Meudt, S., Palm, G., & Schwenker, F. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9719-y · Cited by 10

Minimum variance embedded auto-associative kernel extreme learning machine for one-class classification

Mishra, P. K., Gautam, C., & Tiwari, A. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05905-y · Cited by 10

Modern Machine Learning Methods for Telemetry-Based Spacecraft Health Monitoring

Mukhachev, P., Sadretdinov, T., Pritykin, D., Ivanov, A., & Соловьев, С. В. (2021) · Automation and Remote Control · doi:10.1134/s0005117921080014 · Cited by 10

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures

Turrisi, S., Zappa, E., & Cigada, A. (2022) · Sensors · doi:10.3390/s22062177 · Cited by 10

www.elsevier.com/locate/sigpro Noveltydetection: a review—part 1: statistical approaches

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