Explainable outlier detection: What, for Whom and Why?

Regression shrinkage and selection via the lasso

Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works

Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm

Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works

Greedy function approximation: A gradient boosting machine

Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Least squares quantization in PCM

Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works

A Value for n-person Games

Shapley, L. (1988) · Cambridge University Press eBooks · doi:10.1017/cbo9780511528446.003 · Cited by 10,698 · see citing works

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561 · see citing works

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works

On the generalized distance in statistics

Mahalanobis, P. C. (1936) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 6,068 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207 · see citing works

Proceedings European Conference on Computer Vision 2002

Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153 · see citing works

Maximum likelihood estimation from incomplete data via the EM algorithm

Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,506 · see citing works

LOF: identifying density-based local outliers

Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149 · see citing works

Procedures for Detecting Outlying Observations in Samples

Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works

Identification of Outliers

Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982 · see citing works

Efficient algorithms for mining outliers from large data sets

Ramaswamy, S., Rastogi, R. R., & Shim, K. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335437 · Cited by 1,908 · see citing works

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

Goldstein, A., Kapelner, A., Bleich, J., & Pitkin, E. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.907095 · Cited by 1,861 · see citing works

Efficient algorithms for mining outliers from large data sets

Ramaswamy, S., Rastogi, R., & Shim, K. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335437 · Cited by 1,171 · see citing works

A Survey on the Explainability of Supervised Machine Learning

Burkart, N., & Huber, M. F. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12228 · Cited by 1,008 · see citing works

Quick Shift and Kernel Methods for Mode Seeking

Vedaldi, A., & Soatto, S. (2008) · European Conference on Computer Vision · doi:10.1007/978-3-540-88693-8_52 · Cited by 1,007 · see citing works

Outlier Detection for Temporal Data: A Survey

Gupta, M., Gao, J., Aggarwal, C. C., & Han, J. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.184 · Cited by 997 · see citing works

Angle-based outlier detection in high-dimensional data

Kriegel, H., Schubert, M., & Zimek, A. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401946 · Cited by 865 · see citing works

A survey on unsupervised outlier detection in high‐dimensional numerical data

Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · see citing works

On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study

Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I., & Houle, M. E. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-015-0444-8 · Cited by 744 · see citing works

Explainable machine learning in deployment

Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., & Eckersley, P. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375624 · Cited by 633 · see citing works

"Why Should I Trust You?": Explaining the Predictions of Any ClassifierPreprint

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.04938 · Cited by 587 · see citing works

Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency

Mireille Hildebrandt, C. C., & del Castillo, C. C. (2020) · doi:10.1145/3351095 · Cited by 486 · see citing works

Finding Intensional Knowledge of Distance-Based Outliers

Knorr, E. M., & Ng, R. T. (1999) · Cited by 419 · see citing works

HiCS: High Contrast Subspaces for Density-Based Outlier Ranking

Keller, F., Müller, E., & Böhm, K. (2012) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2012.88 · Cited by 383 · see citing works

An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery

Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108105 · Cited by 355 · see citing works

Generative Adversarial Active Learning for Unsupervised Outlier Detection

Liu, Y., Li, Z., Zhou, C., Jiang, Y., Sun, J., Wang, M., & He, X. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2905606 · Cited by 338 · see citing works

Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01307-2_86 · Cited by 324 · see citing works

Interpreting and Unifying Outlier Scores

Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2011) · SDM · doi:10.1137/1.9781611972818.2 · Cited by 311 · see citing works

Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection

Schubert, E., Zimek, A., & Kriegel, H. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0300-z · Cited by 302 · see citing works

Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

Ras, G., van Gerven, M., & Haselager, P. (2018) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-319-98131-4_2 · Cited by 255 · see citing works

Explainable and Interpretable Models in Computer Vision and Machine Learning

Escalante, H. J., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y., Güçlü, U., & van Gerven, M. (2018) · The Springer series on challenges in machine learning · doi:10.1007/978-3-319-98131-4 · Cited by 203 · see citing works

Outlier ensembles

Aggarwal, C. C. (2013) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2481244.2481252 · Cited by 199 · see citing works

There and back again: Outlier detection between statistical reasoning and data mining algorithms

Zimek, A., & Peter Filzmoser, A. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1280 · Cited by 188 · see citing works

Converting Output Scores from Outlier Detection Algorithms into Probability Estimates

Gao, J., & Tan, P. (2006) · Proceedings · doi:10.1109/icdm.2006.43 · Cited by 142 · see citing works

Knowledge Discovery Via Multiple Models

Domingos, P. (1998) · Intelligent Data Analysis · doi:10.1016/s1088-467x(98)00023-7 · Cited by 140 · see citing works

Comparing Anomaly Detection Techniques for HTTP

Ingham, K. L., & Inoue, H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74320-0_3 · Cited by 128 · see citing works

Outlier Detection in Arbitrarily Oriented Subspaces

Kriegel, H., Kröger, P., Schubert, E., & Zimek, A. (2012) · Proceedings · doi:10.1109/icdm.2012.21 · Cited by 119 · see citing works

Explaining Outliers by Subspace Separability

Micenkova, B., Ng, R. T., Dang, X.-H., & Assent, I. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.132 · Cited by 104 · see citing works

Discovering outlying aspects in large datasets

Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0453-2 · Cited by 83 · see citing works

Explaining Anomalies Detected by Autoencoders Using SHAPPreprint

Antwarg, L., Miller, R. M., Shapira, B., & Rokach, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02407 · Cited by 83 · see citing works

Discriminative features for identifying and interpreting outliers

Dang, X. H., Assent, I., Ng, R. T., Zimek, A., & Schubert, E. (2014) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2014.6816642 · Cited by 79 · see citing works

Contextual Outlier Interpretation

Liu, N., Shin, D., & Hu, X. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/341 · Cited by 74 · see citing works

Mining outlying aspects on numeric data

Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0398-2 · Cited by 70 · see citing works

Finding Unusual Medical Time-Series Subsequences: Algorithms and Applications

Keogh, E., Lin, J., Fu, A. W.-C., & Herle, H. M.-L. V. (2006) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2005.863870 · Cited by 70 · see citing works

Explainable Machine Learning for Fraud Detection

Psychoula, I., Gutmann, A., Mainali, P., Lee, S. H., Dunphy, P., & Petitcolas, F. A. P. (2021) · Computer · doi:10.1109/mc.2021.3081249 · Cited by 68 · see citing works

Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

Pozo, F., & Vidal, Y. (2015) · Energies · doi:10.3390/en9010003 · Cited by 68 · see citing works

Local Outlier Detection with Interpretation

Dang, X. H., Micenková, B., Assent, I., & Ng, R. T. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40994-3_20 · Cited by 56 · see citing works

HOS-Miner

Zhang, J., Lou, M., Ling, T. W., & Wang, H. (2004) · Elsevier eBooks · doi:10.1016/b978-012088469-8.50123-6 · Cited by 52 · see citing works

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadPreprint

Rudin, C. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10154 · Cited by 44 · see citing works

Knowledge Discovery Via Multiple Models

Domingos, P. (1998) · Intelligent Data Analysis · doi:10.3233/ida-1998-2303 · Cited by 43 · see citing works

On the internal evaluation of unsupervised outlier detection

Marques, H. O., Campello, R. J. G. B., Zimek, A., & Sander, J. (2015) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2791347.2791352 · Cited by 38 · see citing works

Outlier ensembles

Agarwal, C. (2013) · doi:10.1145/2500853.2500855 · Cited by 36 · see citing works

Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing

Pozo, F., & Vidal, Y. (2016) · Advances in science and technology · doi:10.4028/www.scientific.net/ast.101.45 · Cited by 33 · see citing works

Toward Accountable Discrimination-Aware Data Mining: The Importance of Keeping the Human in the Loop—and Under the Looking Glass

Berendt, B., & Preibusch, S. (2017) · Big Data · doi:10.1089/big.2016.0055 · Cited by 32 · see citing works

Explainable AI: Using Shapley Value to Explain Complex Anomaly Detection ML-Based Systems

Zou, J., & Petrosian, O. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200777 · Cited by 27 · see citing works

A Novel Method for Detecting Outlying Subspaces in High-dimensional Databases Using Genetic Algorithm

Zhang, J., Gao, Q., & Wang, H. (2006) · Proceedings · doi:10.1109/icdm.2006.6 · Cited by 25 · see citing works

Untitled work

(2004) · doi:10.1016/b978-012088469-8/50123-6 · Cited by 24 · see citing works

Recent Advances in Intrusion Detection

Kruegel, C., Lippmann, R. P., & Clark, A. E. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74320-0 · Cited by 23 · see citing works

Scalable Outlying-Inlying Aspects Discovery via Feature Ranking

Vinh, N. X., Chan, J., Bailey, J., Leckie, C. A., Ramamohanarao, K., & Pei, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-18032-8_33 · Cited by 22 · see citing works

Outlier Detection with Space Transformation and Spectral Analysis

Dang, X. H., Micenková, B., Assent, I., & Ng, R. T. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.25 · Cited by 19 · see citing works

The Potential of AI in Care Optimization: Insights from the User-Driven Co-Development of a Care Integration System

Schneider‐Kamp, A. (2021) · INQUIRY The Journal of Health Care Organization Provision and Financing · doi:10.1177/00469580211017992 · Cited by 17 · see citing works

Machine Learning and Artificial Intelligence

Lefrançois, G. R. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108696333.016 · Cited by 17 · see citing works

Contextual Outlier InterpretationPreprint

Liu, N., Shin, D., & Hu, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10589 · Cited by 13 · see citing works

Knowledge Science, Engineering and Management

(2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99365-2 · Cited by 12 · see citing works

Explainable Detection of Zero Day Web Attacks

Sejr, J. H., Zimek, A., & Schneider–Kamp, P. (2020) · International Conference on Data Intelligence and Security (ICDIS) · doi:10.1109/icdis50059.2020.00016 · Cited by 10 · see citing works

Outlier Detection with Explanations on Music Streaming Data: A Case Study with Danmark Music Group Ltd.

Sejr, J. H., Christiansen, T., Dvinge, N., Hougesen, D., Schneider–Kamp, P., & Zimek, A. (2021) · Applied Sciences · doi:10.3390/app11052270 · Cited by 8 · see citing works

Group Outlying Aspects Mining

Wang, S., Xia, H., Li, G., & Tan, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99365-2_18 · Cited by 5 · see citing works

2020 3rd International Conference on Data Intelligence and Security (ICDIS)

(2020) · doi:10.1109/icdis50059.2020 · Cited by 5 · see citing works

An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery

Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2021) · arXiv · doi:10.48550/arxiv.2102.11848

Explainable Machine Learning for Fraud Detection

Psychoula, I., Gutmann, A., Mainali, P., Lee, S. H., Dunphy, P., & Petitcolas, F. A. P. (2021) · arXiv · doi:10.48550/arxiv.2105.06314

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 · see citing works

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Uddin, M. G., Rahman, A., Taghikhah, F. R., & Olbert, A. I. (2024) · Water Research · doi:10.1016/j.watres.2024.121499 · Cited by 109 · see citing works

Neural network-based surrogate modeling and optimization of a multigeneration system

Ghafariasl, P., Mahmoudan, A., Mohammadi, M., Nazarparvar, A., Hoseinzadeh, S., Fathali, M., Chang, S. I., Zeinalnezhad, M., & Garcia, D. A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123130 · Cited by 85 · see citing works

Feature relevance XAI in anomaly detection: Reviewing approaches and challenges

Tritscher, J., Krause, A., & Andreas Hotho, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1099521 · Cited by 49 · see citing works

Anomaly diagnosis of connected autonomous vehicles: A survey

Fang, Y., Min, H., Wu, X., Wang, W., Zhao, X., Martínez‐Pastor, B., & Teixeira, R. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102223 · Cited by 45 · see citing works

TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios

Barbariol, T., & Susto, G. A. (2022) · Information Sciences · doi:10.1016/j.ins.2022.07.129 · Cited by 32 · see citing works

Explainable correlation-based anomaly detection for Industrial Control Systems

Birihanu, E., & Lendák, I. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1508821 · Cited by 25 · see citing works

Explaining anomalies in coal proximity and coal processing data with Shapley and tree-based models

Liu, X., & Aldrich, C. (2022) · Fuel · doi:10.1016/j.fuel.2022.126891 · Cited by 23 · see citing works

Bayesian active learning isolation forest (B-ALIF): A weakly supervised strategy for anomaly detection

Sartor, D., Barbariol, T., & Susto, G. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107671 · Cited by 18 · see citing works

Active Learning-based Isolation Forest (ALIF): Enhancing anomaly detection with expert feedback

Marcelli, E., Barbariol, T., Sartor, D., & Susto, G. A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121012 · Cited by 17 · see citing works

Human-centric and semantics-based explainable event detection: a survey

Kolajo, T., & Daramola, O. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10525-0 · Cited by 16 · see citing works

Exploring the Quality of Dynamic Open Government Data Using Statistical and Machine Learning Methods

Karamanou, A., Brimos, P., Kalampokis, E., & Tarabanis, K. A. (2022) · Sensors · doi:10.3390/s22249684 · Cited by 16 · see citing works

Improving the Predictive Analytics of Machine-Learning Pipelines for Bridge Infrastructure Asset Management Applications: An Upstream Data Workflow to Address Data Quality Issues in the National Bridge Inventory Database

Hu, X., & Assaad, R. H. (2023) · Journal of Bridge Engineering · doi:10.1061/jbenf2.beeng-6012 · Cited by 14 · see citing works

Throughput Time Predictions Along the Order Fulfilment Process

Hiller, T., Demke, T. M., & Nyhuis, P. (2024) · IEEE Access · doi:10.1109/access.2024.3353029 · Cited by 13 · see citing works

Toward Explainable AI in Spacecraft Health Monitoring: Comparative Benchmarking of Anomaly Detection Models and Open-Source Datasets

Tahir, H., Jabbarpour, M. R., Vo, Q. B., Kowalczyk, R., Barr, J., & Bessell, T. (2025) · IEEE Access · doi:10.1109/access.2025.3639551 · Cited by 7 · see citing works

HEOD: Human-assisted Ensemble Outlier Detection for cybersecurity

Najafi, P., Cheng, F., & Meinel, C. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104040 · Cited by 5 · see citing works

Outlier detection using flexible categorization and interrogative agendas

Boersma, M., Manoorkar, K., Palmigiano, A., Panettiere, M., Tzimoulis, A., & Wijnberg, N. M. (2024) · Decision Support Systems · doi:10.1016/j.dss.2024.114196 · Cited by 5 · see citing works

Explainable Normative Modeling for Brain Disorder Identification in Resting-State fMRI

Shon, Y., Kang, E., Heo, D.-W., & Suk, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3631105 · Cited by 4 · see citing works

Augmenting Circadian Biology Research With Data Science

Soltani, S., Burks, J. H., & Smarr, B. L. (2025) · Journal of Biological Rhythms · doi:10.1177/07487304241310923 · Cited by 4 · see citing works

Knowledge-based quality assurance of a comprehensive set of organ at risk contours for head and neck radiotherapy

Brooks, J., Tryggestad, E. J., Anand, A., Beltran, C. J., Foote, R. L., Lucido, J. J., Laack, N. N., Routman, D. M., Patel, S. H., Seetamsetty, S., et al. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1295251 · Cited by 3 · see citing works

Survey on Explainable AI for Traditional Machine Learning and Domains

Hanif, A., Shawi, R. E., Beheshti, A., & Benatallah, B. (2026) · ACM Computing Surveys · doi:10.1145/3806829 · Cited by 3 · see citing works

Application of Outlier Detection Methods in Audit Analytics

Fulcer, K., Gu, H., Hu, H., Huang, Q. A., Kogan, A., Vasarhelyi, M. A., Wei, D., & Young, J. (2025) · Accounting Horizons · doi:10.2308/horizons-2023-071 · Cited by 2 · see citing works

Detecting Atypical Behaviors of Taxpayers with Risk of Non-Payment in Tax Administration, A Data Mining Framework

Ordoñez, J., & Hallo, M. (2023) · Revista Politécnica · doi:10.33333/rp.vol52n1.04 · Cited by 2 · see citing works

A Deep Autoencoder Based Outlier Detection Model for Intrusion Detection Systems in Wireless Sensor Networks

Shukla, S., Raghuvanshi, A. S., Majumder, S., & Singh, S. (2025) · International Conference on Device Intelligence, Computing and Communication Technologies (DICCT) · doi:10.1109/dicct64131.2025.10986632 · Cited by 1 · see citing works

Human-centered evaluation of statistical parametric mapping and explainable machine learning for outlier detection in plantar pressure data

Dindorf, C., Dully, J., Simon, S., Perchthaler, D., Becker, S. T., Ehmann, H., Heitmann, K. R., Stetter, B. J., Diers, C., & Fröhlich, M. (2026) · Scientific Reports · doi:10.1038/s41598-025-33707-y · Cited by 1 · see citing works

Optimal Scheduling with a Reliable Data Transfer Framework for Drone Inspections of Infrastructures

Mehrooz, G., & Schneider–Kamp, P. (2021) · University of Southern Denmark Research Portal (University of Southern Denmark) · Cited by 1 · see citing works

Explanation models as a component of the intelligent computer-aided diagnosis systems in medicine: a brief review

Utkin, L. V., Krylova, J. Y., & Konstantinov, A. V. (2023) · Russian Journal for Personalized Medicine · doi:10.18705/2782-3806-2022-2-6-23-32

Variable-Length Anomalous Intervals in Data Streams

Leventidis, A. S., Dasu, T., Kanza, Y., & Srivastava, D. (2026) · Journal of Data and Information Quality · doi:10.1145/3786329

An Effective Credit Scoring Model via Advanced Outlier Detection and Balanced Sampling Integration

Pham, C. X., Trinh, H. N., & Tran, L. Q. (2025) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf68649.2025.11365073

Explainable Rank-Based Anomaly Detection

Zafari, B., Ekin, T., & Ruggeri, F. (2026) · The American Statistician · doi:10.1080/00031305.2026.2637640

Détection distribuée d'anomalies dans les flux de données

Togbe, M. U. (2022) · doi:10.70675/d95c1ebfz04f1z4211z862azec8079bbfd36

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Wilson, M. (1992) · Expert Systems with Applications · doi:10.1016/0957-4174(92)90135-f · Cited by 2 · see citing works

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Wu, Y., Akoglu, L., & Davidson, I. A. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02617

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Krishna, B. L. V. S. R., Mahalakshmi, V., & Nookala, G. K. M. (2022) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic53929.2022.9792877

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Panjei, E., Gruenwald, L., Leal, E., Nguyen, C., & Silvia, S. (2022) · The VLDB Journal · doi:10.1007/s00778-021-00721-1 · Cited by 67 · see citing works

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Konijn, R. M., & Kowalczyk, W. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-16248-0_54 · Cited by 7 · see citing works