Outlier Analysis

Comparative Assessment of Process Mining for Supporting IoT Predictive Security

Hemmer, A., Abderrahim, M., Badonnel, R., François, J., & Chrisment, I. (2020) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3038172 · Cited by 16

Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces

Amagata, D., Onizuka, M., & Hara, T. (2022) · The VLDB Journal · doi:10.1007/s00778-022-00729-1 · Cited by 17

Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system

Singh, P., Krishnamoorthy, S., Nayyar, A., Luhach, A. K., & Kaur, A. (2019) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147719883132 · Cited by 17

Detection of Thin Boundaries between Different Types of Anomalies in Outlier Detection Using Enhanced Neural Networks

Kiani, R., Keshavarzi, A., & Bohlouli, M. (2020) · Applied Artificial Intelligence · doi:10.1080/08839514.2020.1722933 · Cited by 16

Factor analysis of mixed data for anomaly detection

Davidow, M., & Matteson, D. S. (2022) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11585 · Cited by 15

Breaking the Curse of Class Imbalance: Bangla Text Classification

Rafi-Ur-Rashid, M., Mahbub, M., & Adnan, M. A. (2022) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3511601 · Cited by 15

Outlier analysis of nonlinear solitary waves for health monitoring applications

Zheng, B., Rizzo, P., & Nasrollahi, A. (2019) · Structural Health Monitoring · doi:10.1177/1475921719876089 · Cited by 15

Customer opinion mining in electricity distribution company using twitter topic modeling and logistic regression

Orji, U., Prasad, R., Odu, N., Ogidiaka, E., & Ibrahim, B. H. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-00890-4 · Cited by 15

An interpretable method for identifying mislabeled commercial building based on temporal feature extraction and ensemble classifier

Xiao, T., Xu, P., Ding, R., & Chen, Z. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103635 · Cited by 14

Autonomous anomaly detection for streaming data

Basheer, M. Y. I., Ali, A. M., Hamid, N. H. A., Ariffin, M. A. M., Osman, R., Nordin, S., & Gu, X. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111235 · Cited by 14

Outlier detection using linguistically quantified statements

Duraj, A., Niewiadomski, A., & Szczepaniak, P. S. (2018) · International Journal of Intelligent Systems · doi:10.1002/int.21924 · Cited by 15

OPHiForest: Order Preserving Hashing Based Isolation Forest for Robust and Scalable Anomaly Detection

Xiang, H., Salcic, Z. A., Dou, W., Xu, X., Qi, L., & Zhang, X. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411988 · Cited by 14

Unsupervised Offline Changepoint Detection Ensembles

Katser, I. D., Kozitsin, V., Lobachev, V., & Maksimov, I. (2021) · Applied Sciences · doi:10.3390/app11094280 · Cited by 14

Root Cause Detection using Dynamic Dependency Graphs from Time Series Data

Shah, S. Y., Dang, X. H., & Zerfos, P. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622059 · Cited by 14

Predictive analytics on IoT

Taneja, T., Jatain, A., & Bajaj, S. B. (2017) · International Conference on Computing, Communication and Automation · doi:10.1109/ccaa.2017.8230000 · Cited by 13

Robust Quality Metric for Scarce Mobile Crowd-Sensing Scenarios

Azmy, S. B., Zorba, N., & Hassanein, H. S. (2018) · IEEE International Conference on Communications workshops · doi:10.1109/iccw.2018.8403744 · Cited by 13

Wisdom of the contexts: active ensemble learning for contextual anomaly detection

Calikus, E., Nowaczyk, S., Bouguelia, M.-R., & Dikmen, O. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00868-7 · Cited by 13

Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks

Dasgupta, D., & Gupta, K. D. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00649-1 · Cited by 16

A Parameter Space Framework for Online Outlier Detection Over High-Volume Data Streams

Zhao, G., Yu, Y., Song, P., Zhao, G., & Ji, Z. (2018) · IEEE Access · doi:10.1109/access.2018.2854836 · Cited by 13

Transformation Based Deep Anomaly Detection in Astronomical Images

Reyes, E., & Estévez, P. A. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206997 · Cited by 13

Abnormal Pattern Prediction: Detecting Fraudulent Insurance Property Claims with Semi-Supervised Machine-Learning

Palacio, S. M. (2019) · Data Science Journal · doi:10.5334/dsj-2019-035 · Cited by 12

A Dynamic Data Driven Applications Systems (DDDAS)-Based Digital Twin IoT Framework

Malik, S., Rouf, R., Mazur, K., & Kontsos, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61725-7_6 · Cited by 12

Spatial Data Quality in the IoT Era: Management and Exploitation

Li, H., Tang, B., Lu, H. C., Cheema, M. A., & Jensen, C. S. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3522568 · Cited by 13

Errors in Time-Series Remote Sensing and an Open Access Application for Detecting and Visualizing Spatial Data Outliers Using Google Earth Engine

Peter, B. G., & Messina, J. P. (2019) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2019.2901404 · Cited by 12

Event-Based Predictive Maintenance on Top of Sensor Data in a Real Industry 4.0 Case Study

Naskos, A., Kougka, G., Toliopoulos, T., Gounaris, A., Vamvalis, C., & Caljouw, D. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-43887-6_28 · Cited by 11

RVAE-ABFA : Robust Anomaly Detection for HighDimensional Data Using Variational Autoencoder

Gao, Y., Shi, B., Dong, B., Chen, Y., Mi, L., Huang, Z., & Shi, Y. (2020) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac48688.2020.0-224 · Cited by 11

ELOF: fast and memory-efficient anomaly detection algorithm in data streams

Yang, Y., Chen, L., & Fan, C. (2020) · Soft Computing · doi:10.1007/s00500-020-05442-1 · Cited by 11

An exploratory study of dynamic foot shape measurements with 4D scanning system

Zhang, L.-Y., Yick, K., Yue, M.-J., Yip, J. Y. W., & Ng, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-35822-0 · Cited by 11

Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection

Akrami, H., Joshi, A. A., Aydöre, S., & Leahy, R. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78191-0_53 · Cited by 10

Active Shooter Detection in Multiple-Person Scenario Using RF-Based Machine Vision

Bazgir, O., Nolte, D., Dhruba, S. R., Li, Y., Li, C., Ghosh, S., & Pal, R. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3028362 · Cited by 10

From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders

Lübbering, M., Ramamurthy, R., Gebauer, M., Bell, T., Sifa, R., & Bauckhage, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61609-0_3 · Cited by 10

Improved incremental local outlier detection for data streams based on the landmark window model

Li, A., Xu, W., Liu, Z., & Shi, Y. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01585-1 · Cited by 10

Human-machine interactive streaming anomaly detection by online self-adaptive forest

Li, Q., Yu, Z., Huang, X., & Guo, B. (2022) · Frontiers of Computer Science · doi:10.1007/s11704-022-1270-y · Cited by 10

Combining KNN with AutoEncoder for Outlier Detection

Liu, S., Ma, S., Chen, H.-Q., Cui, L., & Ding, J. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-2403-y · Cited by 9

An outlier detection algorithm based on the degree of sharpness and its applications on traffic big data preprocessing

Wang, Z., Huang, X., Song, Y., & Xiao, J. (2017) · IEEE International Conference on Big Data Analysis (ICBDA) · doi:10.1109/icbda.2017.8078867 · Cited by 9

Znanstveno-raziskovalni trendi na področju digitalne preobrazbe

Rajkovič, U., & Баггиа, А. (2021) · University of Maribor, University Press eBooks · doi:10.18690/978-961-286-509-2 · Cited by 11

Unsupervised Outlier Detection: A Meta-Learning Algorithm Based on Feature Selection

Papastefanopoulos, V., Linardatos, P., & Kotsiantis, S. B. (2021) · Electronics · doi:10.3390/electronics10182236 · Cited by 9

A Deep Learning Model for Anomalous Wireless Link Detection

Bertalanič, B., Yetgin, H., Cerar, G., & Fortuna, C. (2021) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob52687.2021.9606264 · Cited by 9

Defending Graph Convolutional Networks Against Adversarial Attacks

Ioannidis, V. N., & Giannakis, G. B. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054325 · Cited by 9

Anomaly Detection in Edge Nodes using Sparsity Profile

Moon, A., Zhuo, X., Zhang, J., Son, S. W., & Song, Y. J. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9377757 · Cited by 9

A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data

Rao, A. R., Garai, S., Clarke, D. J. B., & Dey, S. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489448 · Cited by 9

An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

Du, B., Song, J., & Shi, Z. (2019) · Applied Sciences · doi:10.3390/app9081606 · Cited by 9

A Robust Embedding Method for Anomaly Detection on Attributed Networks

Zhang, L., Yuan, J., Liu, Z., Yang, P., & Wang, L. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852354 · Cited by 9

Fatigue Damage Assessment Leveraging Nondestructive Evaluation Data

Mazur, K., Wisner, B. J., & Kontsos, A. (2018) · JOM · doi:10.1007/s11837-018-2882-4 · Cited by 8

Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis

Hasan, M., Carcone, A. I., Naar, S., Eggly, S. S., Alexander, G., Brogan, K. E., & Kotov, A. (2018) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-018-0037-6 · Cited by 8

An Effective Minimal Probing Approach With Micro-Cluster for Distance-Based Outlier Detection in Data Streams

Bah, M. J., Wang, H., Hammad, M. A., Zeshan, F., & Aljuaid, H. (2019) · IEEE Access · doi:10.1109/access.2019.2946966 · Cited by 9

LoPAD: A Local Prediction Approach to Anomaly Detection

Lu, S., Liu, L., Li, J., Le, T. D., & Liu, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-47436-2_50 · Cited by 8

Policy Entropy for Out-of-Distribution Classification

Sedlmeier, A., Müller, R., Illium, S., & Linnhoff–Popien, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61616-8_34 · Cited by 8

One-Class Ensembles for Rare Genomic Sequences Identification

Kaufmann, J., Asalone, K. C., Corizzo, R., Saldanha, C. J., Bracht, J. R., & Japkowicz, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61527-7_23 · Cited by 8

Outlier detection of land surface temperature: Küçükçekmece Region

Kuşak, L., & Küçükali, U. F. (2018) · International Journal of Engineering and Geosciences · doi:10.26833/ijeg.404426 · Cited by 8

ORCA: Outlier detection and Robust Clustering for Attributed graphs

Eswar, S., Kannan, R., Vuduc, R. W., & Park, H. (2021) · Journal of Global Optimization · doi:10.1007/s10898-021-01024-z · Cited by 7

GNN-Based Energy-Efficient Anomaly Detection for IoT Multivariate Time-Series Data

Guo, H., Zhou, Z., & Zhao, D. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10278988 · Cited by 8

Expert-based initialization of recursive mixture estimation

Suzdaleva, E., Nagy, I., & Mlynářová, T. (2016) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2016.7737439 · Cited by 7

Anomalous cluster detection in spatiotemporal meteorological fields

Ramachandra, B., Dutton, B., & Vatsavai, R. R. (2018) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11398 · Cited by 7

Application of Unsupervised Anomaly Detection Techniques to Moisture Content Data from Wood Constructions

Faura, Á. G., Štepec, D., Cankar, M., & Humar, M. (2021) · Forests · doi:10.3390/f12020194 · Cited by 8

Detection and Analysis of Anomalies in People Density and Mobility Through Wireless Smartphone Tracking

Fernández-Ares, A. J., García‐Sánchez, P., Arenas, M. G., Mora, A. M., & Castillo, P. Á. (2020) · IEEE Access · doi:10.1109/access.2020.2979367 · Cited by 7

Robust Locally Weighted Regression for Profile Measurement of Magnesium Alloy Tube in Hot Bending Process

Yao, Z., Wang, R., Jinning, Z., & Shi, Q. (2020) · Mathematical Problems in Engineering · doi:10.1155/2020/7952649 · Cited by 7

A Robust edge detection technique for bone extraction from X-ray images based on image processing techniques

Hassan, N. M. H., & Mahmoud, M. H. M. (2023) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-022-00860-w · Cited by 6

A multi-scale reconstruction method for the anomaly detection in stochastic dynamic networks

Yang, C., Wen, H., Hooi, B., Wu, Y., & Zhou, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.002 · Cited by 6

Adaptive Thresholding Heuristic for KPI Anomaly Detection

Isaac, E. R. H. P., & Sharma, A. (2024) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets59351.2024.10427016 · Cited by 6

Bootstrap-Based Quality Metric for Scarce Sensing Systems

Azmy, S. B., Zorba, N., & Hassanein, H. S. (2018) · Globecom · doi:10.1109/glocom.2018.8647536 · Cited by 6

NIT: Searching for rumors in social network through neighborhood information transmission

Wang, B., Wei, H., Liu, S., Wang, K., & Li, R. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126552 · Cited by 6

Elicitation of Candidate Subspaces in High-Dimensional Data

Thudumu, S., Branch, P., Jin, J., & Singh, J. (2019) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2019.00275 · Cited by 6

Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine

Qu, H., Zhou, J., Qin, J., & Tian, X. (2020) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421500129 · Cited by 6

Identifying Anomalies While Preserving Privacy

Asif, H. S., Vaidya, J. S., & Papakonstantinou, P. A. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3129633 · Cited by 7

Gen2Out: Detecting and Ranking Generalized Anomalies

Lee, M.-C., Shekhar, S., Faloutsos, C., Hutson, T. N., & Iasemidis, L. D. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671550 · Cited by 9

An Ensemble Angle-Based Outlier Detection for Big Data

Al-taei, R., & Haeri, M. A. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-33495-6_8 · Cited by 6

Parameterless Semi-supervised Anomaly Detection in Univariate Time Series

Iegorov, O., & Fischmeister, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67658-2_37 · Cited by 6

Anomaly Detection Techniques in Smart City: A Review from a Framework Perspective

Prabowo, O. M., Supangkat, S. H., & Mulyana, E. (2021) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss53185.2021.9533252 · Cited by 6

Detection of Black Regions in the Forex Market By Analyzing High-Frequency Intraday Data

Weeraddana, N. R., Silva, T., & Jayathilake, P. W. D. C. (2018) · International Conference on Advances in ICT for Emerging Regions · doi:10.1109/icter.2018.8615500 · Cited by 5

Saliency-guided meta-hallucinator for few-shot learning

Zhang, H., Liu, C., Wang, J., Ma, L., Koniusz, P., Torr, P. H. S., & Yang, L. (2024) · Science China Information Sciences · doi:10.1007/s11432-023-4113-1 · Cited by 5

Data Cleaning and Classification in the Presence of Label Noise with Class-Specific Autoencoder

Zhang, W., Wang, D., & Tan, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92537-0_30 · Cited by 5

Removing Outliers from 3D Macrotexture Data by Controlling False Discovery Rate

Bongioanni, V. I., Katicha, S. W., & Flintsch, G. W. (2019) · Journal of Transportation Engineering Part B Pavements · doi:10.1061/jpeodx.0000119 · Cited by 5

Comparative analysis of resource utilization in integrative anthroposophic and all German pediatric inpatient departments

Fetz, K., Längler, A., Schwermer, M., Carvalho-Hilje, C., Vagedes, J., Zuzak, T. J., & Ostermann, T. (2020) · BMC Health Services Research · doi:10.1186/s12913-020-05782-6 · Cited by 5

The deep fusion of topological structure and attribute information for anomaly detection in attributed networks

Su, J., Dong, Y., Qian, J., Xin, Y., & Pan, J. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02386-3 · Cited by 5

Robust Switching Regressions Using the Laplace Distribution

Lu, K.-P., & Chang, S.-T. (2022) · Mathematics · doi:10.3390/math10244722 · Cited by 5

Fraudster Detection Based on Modularity Optimization Algorithm

Luan, T., Yan, Z., & Zhang, S. (2019) · International Conference on Computer Supported Cooperative Work in Design (CSCWD) · doi:10.1109/cscwd.2019.8791908 · Cited by 5

Dynamic Attack Scoring Using Distributed Local Detectors

Zohrevand, Z., & Glässer, U. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054264 · Cited by 5

A Dual-Layer Architecture for the Protection of Medical Devices from Anomalous Instructions

Mahler, T., Shalom, E., Elovici, Y., & D, Y. S. M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_25 · Cited by 5

GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs

Bhatia, S., Wang, Y., Hooi, B., & Chakraborty, T. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86520-7_3 · Cited by 5

Proposing a new local density estimation outlier detection algorithm: an empirical case study on flow pattern experiments

Mahmoodi, K., Ketabdari, M. J., & Vaghefi, M. (2021) · Pattern Analysis and Applications · doi:10.1007/s10044-021-01019-2 · Cited by 5

Using Negative Detectors for Identifying Adversarial Data Manipulation in Machine Learning

Gupta, K. D., & Dasgupta, D. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533790 · Cited by 5

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

Sergin, N. D., Hu, J., Li, Z., Zhang, C., Tsung, F., & Yan, H. (2024) · INFORMS Journal on Data Science · doi:10.1287/ijds.2022.0028 · Cited by 4

Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs

Ioannidis, V. N., Berberidis, D., & Giannakis, G. B. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414953 · Cited by 4

Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering

Ge, G., Guan, Q., Wu, L., Luo, W., & Zhu, X. (2022) · Journal of Data Analysis and Information Processing · doi:10.4236/jdaip.2022.101002 · Cited by 4

Da mordida à curtida: a influência do grau de uso corporativo e repercussão nas mídias sociais na recomendação online dos clientes de bares e restaurantes

Romeu, L. P., Lopes, E. L., Brand�ão, M. M., & Pinochet, L. H. C. (2022) · ReMark - Revista Brasileira de Marketing · doi:10.5585/remark.v21i2.19077 · Cited by 4

Unsupervised Identification of Abnormal Nodes and Edges in Graphs

Senaratne, A., Christen, P., Williams, G., & Omran, P. G. (2022) · Journal of Data and Information Quality · doi:10.1145/3546912 · Cited by 4

Combating economic cybercrime using artificial intelligence (AI)

Bukowski, M. (2023) · PRZEGLĄD POLICYJNY · doi:10.5604/01.3001.0053.9746 · Cited by 4

Effects of People Equity and Entrepreneurial Orientation on Firm Performance: The Mediation Role of Social Capital

Gill, S. A., Raza, A., Farooq, K., Alhoul, M. A. Y. A., Alshebami, A. S., & Marri, S. H. A. (2024) · Journal of the Knowledge Economy · doi:10.1007/s13132-024-01842-y · Cited by 4

Time-Series to Image-Transformed Adversarial Autoencoder for Anomaly Detection

Kang, J., Kim, M., Park, J., & Park, S. (2024) · IEEE Access · doi:10.1109/access.2024.3450709 · Cited by 7

Music Outlier Detection Using Multiple Sequence Alignment and Independent Ensembles

Bountouridis, D., Koops, H. V., Wiering, F., & Veltkamp, R. C. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46759-7_22 · Cited by 4

High-dimensional cluster boundary detection using directed Markov tree

Cao, X. (2020) · Pattern Analysis and Applications · doi:10.1007/s10044-020-00897-2 · Cited by 4

AutoMTS: Fully Autonomous Processing of Multivariate Time Series Data from Heterogeneous Sensor Networks

Sousa, R., Amado, C., & Henriques, R. (2021) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-77569-8_12 · Cited by 4

Erroneous High Occupancy Vehicle Lane Data: Detecting Misconfigured Traffic Sensors With Machine Learning

Fournier, N., Farid, Y. Z., & Patire, A. D. (2022) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981221126515 · Cited by 4

Information Processing Factory 2.0 - Self-awareness for Autonomous Collaborative Systems

Sperling, N., Bendrick, A., Stöhrmann, D., Ernst, R., Donyanavard, B., Maurer, F., Lenke, O., Surhonne, A. P., Herkersdorf, A., Amer, W., et al. (2023) · Design, Automation, and Test in Europe · doi:10.23919/date56975.2023.10137006 · Cited by 4

Anomaly detection in data streams based on graph coloring density coefficients

Tripathi, A. M., & Baruah, R. D. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7850116 · Cited by 4

Incorporating Privileged Information to Unsupervised Anomaly Detection

Shekhar, S., & Akoglu, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_6 · Cited by 4

Unsupervised Algorithms for Microarray Sample Stratification

Fratello, M., Cattelani, L., Federico, A., Pavel, A., Scala, G., Serra, A., & Greco, D. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1839-4_9 · Cited by 4

Tree-Based Cost Sensitive Methods for Fraud Detection in Imbalanced Data

Metzler, G., Badiche, X., Belkasmi, B., Fromont, É., Habrard, A., & Sebban, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01768-2_18 · Cited by 3

Fraudster Detection Based on Label Propagation Algorithm

Luan, T., Yan, Z., Zhang, S., & Zheng, Y. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00077 · Cited by 3

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