Statistical Process Monitoring of Artificial Neural Networks

References

• Abraham, Y., Gerrits, B., Ludwig, M.-G., Rebhan, M., and Gubser Keller, C. (2017), “Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations,” Cytometry Part B: Clinical Cytometry, 92, 42–56. DOI: 10.1002/cyto.b.21499.
   PubMed Web of Science ®Google Scholar
• Aldridge, I., and Avellaneda, M. (2019), “Neural Networks in Finance: Design and Performance,” The Journal of Financial Data Science, 1, 39–62. DOI: 10.3905/jfds.2019.1.4.039.
   Google Scholar
• Ali, S., Pievatolo, A., and Göb, R. (2016), “An Overview of Control Charts for High-Quality Processes,” Quality and Reliability Engineering International, 32, 2171–2189. DOI: 10.1002/qre.1957.
   Web of Science ®Google Scholar
• Angermueller, C., Pärnamaa, T., Parts, L., and Stegle, O. (2016), “Deep Learning for Computational Biology,” Molecular Systems Biology, 12, 878. DOI: 10.15252/msb.20156651.
   PubMed Web of Science ®Google Scholar
• Apsemidis, A., Psarakis, S., and Moguerza, J. M. (2020), “A Review of Machine Learning Kernel Methods in Statistical Process Monitoring,” Computers & Industrial Engineering, 142, 106376. DOI: 10.1016/j.cie.2020.106376.
   Web of Science ®Google Scholar
• Baena-Garc ia, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavalda, R., and Morales-Bueno, R. (2006), “Early Drift Detection Method,” in Fourth International Workshop on Knowledge Discovery from Data Streams (Vol. 6), pp. 77–86.
   Google Scholar
• Ball, N. M., Brunner, R. J., Myers, A. D., and Tcheng, D. (2006), “Robust Machine Learning Applied to Astronomical Data Sets. I. Star-galaxy Classification of the Sloan Digital Sky Survey DR3 using Decision Trees,” The Astrophysical Journal, 650, 497. DOI: 10.1086/507440.
   Web of Science ®Google Scholar
• Barale, M., and Shirke, D. (2019), “Nonparametric Control Charts based on Data Depth for Location Parameter,” Journal of Statistical Theory and Practice, 13, 1–19. DOI: 10.1007/s42519-019-0041-z.
   Web of Science ®Google Scholar
• Baranowski, J., Dudek, A., and Mularczyk, R. (2021), “Transient Anomaly Detection Using Gaussian Process Depth Analysis,” in 2021 25th International Conference on Methods and Models in Automation and Robotics (MMAR), pp. 221–226, IEEE. DOI: 10.1109/MMAR49549.2021.9528470.
   Google Scholar
• Bell, R. C., Jones-Farmer, L. A., and Billor, N. (2014), “A Distribution-Free Multivariate Phase I Location Control Chart for Subgrouped Data from Elliptical Distributions,” Technometrics, 56, 528–538. DOI: 10.1080/00401706.2013.879264.
   Web of Science ®Google Scholar
• Bifet, A., and Gavalda, R. (2007), “Learning from Time-Changing Data with Adaptive Windowing,” in Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 443–448.
   Google Scholar
• Bifet, A., Gavalda, R., Holmes, G., and Pfahringer, B. (2018), Machine Learning for Data Streams: With Practical Examples in MOA, Cambridge, MA: MIT Press.
   Google Scholar
• Boone, J., and Chakraborti, S. (2012), “Two Simple Shewhart-Type Multivariate Nonparametric Control Charts,” Applied Stochastic Models in Business and Industry, 28, 130–140. DOI: 10.1002/asmb.900.
   Web of Science ®Google Scholar
• Breiman, L., Meisel, W., and Purcell, E. (1977), “Variable Kernel Estimates of Multivariate Densities,” Technometrics, 19, 135–144. DOI: 10.1080/00401706.1977.10489521.
   Web of Science ®Google Scholar
• Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J. (2000), “LOF: Ídentifying Density-based Local Outliers,” in Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104.
   Google Scholar
• Caro, L. D., Frias-Martinez, V., and Frias-Martinez, E. (2010), “Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz,” in Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 125–132, Springer.
   Google Scholar
• Cascos, I., and López-Díaz, M. (2018), “Control Charts based on Parameter Depths,” Applied Mathematical Modelling, 53, 487–509. DOI: 10.1016/j.apm.2017.09.009.
   Web of Science ®Google Scholar
• Celano, G., Castagliola, P., Fichera, S., and Nenes, G. (2013), “Performance of t Control Charts in Short Runs with Unknown Shift Sizes,” Computers & Industrial Engineering, 64, 56–68. DOI: 10.1016/j.cie.2012.10.003.
   Web of Science ®Google Scholar
• Chiroma, H., Abdullahi, U. A., Alarood, A. A., Gabralla, L. A., Rana, N., Shuib, L., Hashem, I. A. T., Gbenga, D. E., Abubakar, A. I., Zeki, A. M., et al. (2018), “Progress on Artificial Neural Networks for Big Data Analytics: A Survey,” IEEE Access, 7, 70535–70551. DOI: 10.1109/ACCESS.2018.2880694.
   Web of Science ®Google Scholar
• Cook, R. D., and Ni, L. (2005), “Sufficient Dimension Reduction via Inverse Regression: A Minimum Discrepancy Approach,” Journal of the American Statistical Association, 100, 410–428. DOI: 10.1198/016214504000001501.
   Web of Science ®Google Scholar
• Corbière, C., Thome, N., Bar-Hen, A., Cord, M., and Pérez, P. (2019), “Addressing Failure Prediction by Learning Model Confidence,” in Advances in Neural Information Processing Systems (Vol. 32).
   Google Scholar
• Dang, X., and Serfling, R. (2010), “Nonparametric Depth-based Multivariate Outlier Identifiers, and Masking Robustness Properties,” Journal of Statistical Planning and Inference, 140, 198–213. DOI: 10.1016/j.jspi.2009.07.004.
   Web of Science ®Google Scholar
• Demšar, J., and Bosnić, Z. (2018), “Detecting Concept Drift in Data Streams Using Model Explanation,” Expert Systems with Applications, 92, 546–559. DOI: 10.1016/j.eswa.2017.10.003.
   Web of Science ®Google Scholar
• Donoho, D. L., and Gasko, M. (1992), “Breakdown Properties of Location Estimates based on Halfspace Depth and Projected Outlyingness,” The Annals of Statistics, 20, 1803–1827. DOI: 10.1214/aos/1176348890.
   Web of Science ®Google Scholar
• Dyckerhoff, R., Mozharovskyi, P., and Nagy, S. (2021), “Approximate Computation of Projection Depths,” Computational Statistics & Data Analysis, 157, 107166. DOI: 10.1016/j.csda.2020.107166.
   Web of Science ®Google Scholar
• Emambocus, B. A. S., Jasser, M. B., and Amphawan, A. (2023), “A Survey on the Optimization of Artificial Neural Networks using Swarm Intelligence Algorithms,” IEEE Access, 11, 1280–1294. DOI: 10.1109/ACCESS.2022.3233596.
   Web of Science ®Google Scholar
• Fang, Z., Li, Y., Lu, J., Dong, J., Han, B., and Liu, F. (2022), “Is Out-of-Distribution Detection Learnable?” arXiv preprint arXiv:2210.14707.
   Google Scholar
• Forrest, S., Perelson, A. S., Allen, L., and Cherukuri, R. (1994), “Self-Nonself Discrimination in a Computer,” in Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy, pp. 202–212, IEEE.
   Google Scholar
• Fort, S., Ren, J., and Lakshminarayanan, B. (2021), “Exploring the Limits of Out-of-Distribution Detection,” in Advances in Neural Information Processing Systems (Vol. 34), pp. 7068–7081.
   Google Scholar
• Francisci, G., Nieto-Reyes, A., and Agostinelli, C. (2019), “Generalization of the Simplicial Depth: No Vanishment Outside the Convex Hull of the Distribution Support,” arXiv preprint arXiv:1909.02739.
   Google Scholar
• Gama, J., Medas, P., Castillo, G., and Rodrigues, P. (2004), “Learning with Drift Detection,” in Brazilian Symposium on Artificial Intelligence, pp. 286–295, Springer.
   Google Scholar
• Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., and Bouchachia, A. (2014), “A Survey on Concept Drift Adaptation,” ACM Computing Surveys (CSUR), 46, 1–37. DOI: 10.1145/2523813.
   Web of Science ®Google Scholar
• Gao, X., Pishdad-Bozorgi, P., Shelden, D. R., and Hu, Y. (2019), “Machine Learning Applications in Facility Life-Cycle Cost Analysis: A Review,” Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, pp. 267–274.
   Google Scholar
• Garcia, K. D., Poel, M., Kok, J. N., and de Carvalho, A. C. (2019), “Online Clustering for Novelty Detection and Concept Drift in Data Streams,” in EPIA Conference on Artificial Intelligence, pp. 448–459, Springer.
   Google Scholar
• Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al. (2021), “A Survey of Uncertainty in Deep Neural Networks,” arXiv preprint arXiv:2107.03342.
   Google Scholar
• Gemaque, R. N., Costa, A. F. J., Giusti, R., and Dos Santos, E. M. (2020), “An Overview of Unsupervised Drift Detection Methods,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10, e1381.
   Web of Science ®Google Scholar
• Ghazikhani, A., Monsefi, R., and Yazdi, H. S. (2013), “Ensemble of Online Neural Networks for Non-stationary and Imbalanced Data Streams,” Neurocomputing, 122, 535–544. DOI: 10.1016/j.neucom.2013.05.003.
   Web of Science ®Google Scholar
• Ghosh, A. K., Chaudhuri, P., and Sengupta, D. (2006), “Classification Using Kernel Density Estimates: Multiscale Analysis and Visualization,” Technometrics, 48, 120–132. DOI: 10.1198/004017005000000391.
   Web of Science ®Google Scholar
• Goldstein, M., and Uchida, S. (2016), “A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data,” PloS One, 11, e0152173. DOI: 10.1371/journal.pone.0152173.
   PubMed Web of Science ®Google Scholar
• Goodfellow, I., Bengio, Y., and Courville, A. (2016), Deep Learning, Cambridge, MA: MIT Press.
   Google Scholar
• Guttormsson, S. E., Marks, R., El-Sharkawi, M., and Kerszenbaum, I. (1999), “Elliptical Novelty Grouping for On-line Short-Turn Detection of Excited Running Rotors,” IEEE Transactions on Energy Conversion, 14, 16–22. DOI: 10.1109/60.749142.
   Web of Science ®Google Scholar
• Haque, A., Khan, L., Baron, M., Thuraisingham, B., and Aggarwal, C. (2016), “Efficient Handling of Concept Drift and Concept Evolution over Stream Data,” in 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 481–492. IEEE. DOI: 10.1109/ICDE.2016.7498264.
   Google Scholar
• Heipke, C., and Rottensteiner, F. (2020), “Deep Learning for Geometric and Semantic Tasks in Photogrammetry and Remote Sensing,” Geo-spatial Information Science, 23, 10–19. DOI: 10.1080/10095020.2020.1718003.
   Web of Science ®Google Scholar
• Hendrycks, D., and Gimpel, K. (2016), “A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks,” arXiv preprint arXiv:1610.02136.
   Google Scholar
• Hermans, M., and Schrauwen, B. (2013), “Training and Analysing Deep Recurrent Neural Networks,” in Advances in Neural Information Processing Systems (Vol. 26), pp. 190–198.
   Google Scholar
• Hoffman, P., Grinstein, G., and Pinkney, D. (1999), “Dimensional Anchors: A Graphic Primitive for Multidimensional Multivariate Information Visualizations,” in Proceedings of the 1999 Workshop on New Paradigms in Information Visualization and Manipulation in Conjunction with the Eighth ACM International Conference on Information and Knowledge Management, pp. 9–16.
   Google Scholar
• Hu, H., Kantardzic, M., and Sethi, T. S. (2020), “No Free Lunch Theorem for Concept Drift Detection in Streaming Data Classification: A Review,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10, e1327.
   Web of Science ®Google Scholar
• Huang, J., Zhu, Q., Yang, L., and Feng, J. (2016), “A Non-parameter Outlier Detection Algorithm based on Natural Neighbor,” Knowledge-Based Systems, 92, 71–77. DOI: 10.1016/j.knosys.2015.10.014.
   Web of Science ®Google Scholar
• Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. D. (2011), “Adversarial Machine Learning,” in Proceedings of the 4th ACM workshop on Security and Artificial Intelligence, pp. 43–58. DOI: 10.1145/2046684.2046692.
   Google Scholar
• Ivanovs, J., and Mozharovskyi, P. (2021), “Distributionally Robust Halfspace Depth,” arXiv preprint arXiv:2101.00726.
   Google Scholar
• Jones-Farmer, L. A., Woodall, W. H., Steiner, S. H., and Champ, C. W. (2014), “An Overview of Phase I Analysis for Process Improvement and Monitoring,” Journal of Quality Technology, 46, 265–280. DOI: 10.1080/00224065.2014.11917969.
   Web of Science ®Google Scholar
• Kan, S. H. (2003), Metrics and Models in Software Quality Engineering, Reading, MA: Addison-Wesley Professional.
   Google Scholar
• Kim, Y., and Park, C. H. (2017), “An Efficient Concept Drift Detection Method for Streaming Data Under Limited Labeling,” IEICE Transactions on Information and Systems, 100, 2537–2546. DOI: 10.1587/transinf.2017EDP7091.
   Web of Science ®Google Scholar
• Klinkenberg, R., and Joachims, T. (2000), “Detecting Concept Drift with Support Vector Machines,” in ICML, pp. 487–494.
   Google Scholar
• Klinkenberg, R., and Renz, I. (1998), “Adaptive Information Filtering: Learning in the Presence of Concept Drifts,” Learning for Text Categorization, pp. 33–40.
   Google Scholar
• Krawczyk, B., and Woźniak, M. (2015), “One-Class Classifiers with Incremental Learning and Forgetting for Data Streams with Concept Drift,” Soft Computing, 19, 3387–3400. DOI: 10.1007/s00500-014-1492-5.
   Web of Science ®Google Scholar
• Krizhevsky, A., Hinton, G., et al. (2009), “Learning Multiple Layers of Features from Tiny Images,” Toronto, ON, Canada.
   Google Scholar
• Kuncheva, L. I. (2009), “Using Control Charts for Detecting Concept Change in Streaming Data,” Bangor University, p. 48.
   Google Scholar
• Lange, T., Mosler, K., and Mozharovskyi, P. (2014), “Fast Nonparametric Classification based on Data Depth,” Statistical Papers, 55, 49–69. DOI: 10.1007/s00362-012-0488-4.
   Web of Science ®Google Scholar
• Lee, K., Lee, K., Lee, H., and Shin, J. (2018), “A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks,” in Advances in Neural Information Processing Systems (Vol. 31).
   PubMedGoogle Scholar
• Lee, S., Kwak, M., Tsui, K.-L., and Kim, S. B. (2019), “Process Monitoring Using Variational Autoencoder for High-Dimensional Nonlinear Processes,” Engineering Applications of Artificial Intelligence, 83, 13–27. DOI: 10.1016/j.engappai.2019.04.013.
   Web of Science ®Google Scholar
• Li, J., Cuesta-Albertos, J. A., and Liu, R. Y. (2012), “DD-classifier: Nonparametric Classification Procedure based on DD-plot,” Journal of the American Statistical Association, 107, 737–753. DOI: 10.1080/01621459.2012.688462.
   Web of Science ®Google Scholar
• Li, K.-C. (1991), “Sliced Inverse Regression for Dimension Reduction,” Journal of the American Statistical Association, 86, 316–327. DOI: 10.1080/01621459.1991.10475035.
   Web of Science ®Google Scholar
• Li, L. (2007), “Sparse Sufficient Dimension Reduction,” Biometrika, 94, 603–613. DOI: 10.1093/biomet/asm044.
   Web of Science ®Google Scholar
• Li, L., and Nachtsheim, C. J. (2006), “Sparse Sliced Inverse Regression,” Technometrics, 48, 503–510. DOI: 10.1198/004017006000000129.
   Web of Science ®Google Scholar
• Li, P., Wu, X., Hu, X., and Wang, H. (2015), “Learning Concept-Drifting Data Streams with Random Ensemble Decision Trees,” Neurocomputing, 166, 68–83. DOI: 10.1016/j.neucom.2015.04.024.
   Web of Science ®Google Scholar
• Lin, Q., Zhao, Z., and Liu, J. S. (2019), “Sparse Sliced Inverse Regression via Lasso,” Journal of the American Statistical Association, 114, 1726–1739. DOI: 10.1080/01621459.2018.1520115.
   PubMed Web of Science ®Google Scholar
• Liu, F. T., Ting, K. M., and Zhou, Z.-H. (2008), “Isolation Forest,” in 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422.
   Google Scholar
• Liu, R. Y. (1990), “On a Notion of Data Depth based on Random Simplices,” The Annals of Statistics, pp. 405–414. DOI: 10.1214/aos/1176347507.
   Web of Science ®Google Scholar
• Liu, R. Y. (1995), “Control Charts for Multivariate Processes,” Journal of the American Statistical Association, 90, 1380–1387.
   Web of Science ®Google Scholar
• Liu, R. Y., Serfling, R. J., and Souvaine, D. L. (2006), Data Depth: Robust Multivariate Analysis, Computational Geometry, and Applications (Vol. 72), Providence, RI: American Mathematical Society.
   Google Scholar
• Liu, R. Y., and Singh, K. (1993), “A Quality Index based on Data Depth and Multivariate Rank Tests,” Journal of the American Statistical Association, 88, 252–260. DOI: 10.2307/2290720.
   Web of Science ®Google Scholar
• Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2018), “Learning Under Concept Drift: A Review,” IEEE Transactions on Knowledge and Data Engineering, 31, 2346–2363. DOI: 10.1109/TKDE.2018.2876857.
   Web of Science ®Google Scholar
• Mahalanobis, P. C. (1936), “On the Generalised Distance in Statistics,” in Proceedings of the National Institute of Sciences of India (Vol. 2), pp. 49–55.
   Google Scholar
• Markou, M., and Singh, S. (2003a), “Novelty Detection: A Review—Part 1: Statistical Approaches,” Signal Processing, 83, 2481–2497. DOI: 10.1016/j.sigpro.2003.07.018.
   Web of Science ®Google Scholar
• Markou, M., and Singh, S. (2003b), “Novelty Detection: A Review—Part 2: Neural Network based Approaches,” Signal Processing, 83, 2499–2521.
   Web of Science ®Google Scholar
• Masud, M. M., Gao, J., Khan, L., Han, J., and Thuraisingham, B. (2009), “Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams,” in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II 20, pp. 79–94, Springer.
   Google Scholar
• Mejri, D., Limam, M., and Weihs, C. (2017), “Combination of Several Control Charts Based on Dynamic Ensemble Methods,” Mathematics and Statistics, 5, 117–129. DOI: 10.13189/ms.2017.050302.
   Google Scholar
• Mejri, D., Limam, M., and Weihs, C. (2021), “A New Time Adjusting Control Limits Chart for Concept Drift Detection,” IFAC Journal of Systems and Control, 17, 100170. DOI: 10.1016/j.ifacsc.2021.100170.
   Google Scholar
• Montgomery, D. C. (2020), Introduction to Statistical Quality Control, Hoboken, NJ: Wiley.
   Google Scholar
• Moon, J., Kim, J., Shin, Y., and Hwang, S. (2020), “Confidence-Aware Learning for Deep Neural Networks,” in Proceedings of the 37th International Conference on Machine Learning (Vol. 119), pp. 7034–7044, PMLR.
   Google Scholar
• Mosler, K. (2013), “Depth Statistics,” in Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather, eds. C. Becker, R. Fried, S. Kuhnt, pp. 17–34, Berlin: Springer.
   Google Scholar
• Mosler, K., and Mozharovskyi, P. (2022), “Choosing Among Notions of Multivariate Depth Statistics,” Statistical Science, 37, 348–368. DOI: 10.1214/21-STS827.
   Web of Science ®Google Scholar
• Mozharovskyi, P. (2022), “Anomaly Detection Using Data Depth: Multivariate Case,” arXiv preprint arXiv:2210.02851.
   Google Scholar
• Nishida, K., and Yamauchi, K. (2007), “Detecting Concept Drift Using Statistical Testing,” in Discovery Science (Vol. 4755), eds. V. Corruble, M. Takeda, and E. Suzuki, pp. 264–269, Berlin: Springer.
   Google Scholar
• O’Shea, K., and Nash, R. (2015), “An Introduction to Convolutional Neural Networks,” arXiv preprint arXiv:1511.08458.
   Google Scholar
• Otter, D. W., Medina, J. R., and Kalita, J. K. (2020), “A Survey of the Usages of Deep Learning for Natural Language Processing,” IEEE Transactions on Neural Networks and Learning Systems, 32, 604–624. DOI: 10.1109/TNNLS.2020.2979670.
   Web of Science ®Google Scholar
• Pandolfo, G., Iorio, C., Staiano, M., Aria, M., and Siciliano, R. (2021), “Multivariate Process Control Charts based on the Lp Depth,” Applied Stochastic Models in Business and Industry, 37, 229–250. DOI: 10.1002/asmb.2616.
   Web of Science ®Google Scholar
• Parekh, J., Mozharovskyi, P., and d’ Alché-Buc, F. (2021), “A Framework to Learn with Interpretation,” in Advances in Neural Information Processing Systems (Vol. 34), eds. M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, and J. W. Vaughan, pp. 24273–24285, Red Hook, NY: Curran Associates, Inc.
   Google Scholar
• Parzen, E. (1962), “On Estimation of a Probability Density Function and Mode,” The Annals of Mathematical Statistics, 33, 1065–1076. DOI: 10.1214/aoms/1177704472.
   Google Scholar
• Pearce, T., Brintrup, A., and Zhu, J. (2021), “Understanding Softmax Confidence and Uncertainty,” arXiv preprint arXiv:2106.04972.
   Google Scholar
• Perdikis, T., and Psarakis, S. (2019), “A Survey on Multivariate Adaptive Control Charts: Recent Developments and Extensions,” Quality and Reliability Engineering International, 35, 1342–1362. DOI: 10.1002/qre.2521.
   Web of Science ®Google Scholar
• Piano, L., Garcea, F., Gatteschi, V., Lamberti, F., and Morra, L. (2022), “Detecting Drift in Deep Learning: A Methodology Primer,” IT Professional, 24, 53–60. DOI: 10.1109/MITP.2022.3191318.
   Web of Science ®Google Scholar
• Pidhorskyi, S., Almohsen, R., and Doretto, G. (2018), “Generative Probabilistic Novelty Detection with Adversarial Autoencoders,” in Advances in Neural Information Processing Systems (Vol. 31).
   Google Scholar
• Pokotylo, O., Mozharovskyi, P., and Dyckerhoff, R. (2019), “Depth and Depth-Based Classification with R Package ddalpha,” Journal of Statistical Software, 91, 1–46. DOI: 10.18637/jss.v091.i05.
   Web of Science ®Google Scholar
• Psarakis, S. (2011), “The Use of Neural Networks in Statistical Process Control Charts,” Quality and Reliability Engineering International, 27, 641–650. DOI: 10.1002/qre.1227.
   Web of Science ®Google Scholar
• Psarakis, S. (2015), “Adaptive Control Charts: Recent Developments and Extensions,” Quality and Reliability Engineering International, 31, 1265–1280.
   Web of Science ®Google Scholar
• Qiu, P. (2014), Introduction to Statistical Process Control, Boca Raton, FL: CRC Press.
   Google Scholar
• Roberts, S., and Tarassenko, L. (1994), “A Probabilistic Resource Allocating Network for Novelty Detection,” Neural Computation, 6, 270–284. DOI: 10.1162/neco.1994.6.2.270.
   Web of Science ®Google Scholar
• Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., and Williamson, R. (2001), “Estimating the Support of a High-Dimensional Distribution,” Neural Computation, 13, 1443–1471. DOI: 10.1162/089976601750264965.
   PubMed Web of Science ®Google Scholar
• Schubert, E., Zimek, A., and Kriegel, H.-P. (2014), “Generalized Outlier Detection with Flexible Kernel Density Estimates,” in Proceedings of the 2014 SIAM International Conference on Data Mining, pp. 542–550, SIAM. DOI: 10.1137/1.9781611973440.63.
   Google Scholar
• Sergin, N. D., and Yan, H. (2021), “Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders,” Journal of Quality Technology, 53, 1–46. DOI: 10.1080/00224065.2021.1903821.
   Web of Science ®Google Scholar
• Shrestha, A., and Mahmood, A. (2019), “Review of Deep Learning Algorithms and Architectures,” IEEE Access, 7, 53040–53065. DOI: 10.1109/ACCESS.2019.2912200.
   Web of Science ®Google Scholar
• Smith, L. N. (2018), “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1–learning Rate, Batch Size, Momentum, and Weight Decay,” arXiv preprint arXiv:1803.09820.
   Google Scholar
• Stoumbos, Z. G., Jones, L. A., Woodall, W. H., and Reynolds, M. R. (2001), “On Nonparametric Multivariate Control Charts based on Data Depth,” in Frontiers in Statistical Quality Control 6, eds. H.-J. Lenz, P.-T. Wilrich, pp. 207–227, Heidelberg: Springer.
   Google Scholar
• Struyf, A. J., and Rousseeuw, P. J. (1999), “Halfspace Depth and Regression Depth Characterize the Empirical Distribution,” Journal of Multivariate Analysis, 69, 135–153. DOI: 10.1006/jmva.1998.1804.
   Web of Science ®Google Scholar
• Sun, Y., Ming, Y., Zhu, X., and Li, Y. (2022), “Out-of-Distribution Detection with Deep Nearest Neighbors,” in International Conference on Machine Learning, pp. 20827–20840. PMLR.
   Google Scholar
• Tukey, J. W. (1975), “Mathematics and the Picturing of Data,” in Proceedings of the International Congress of Mathematicians, Vancouver, 1975 (Vol. 2), pp. 523–531.
   Google Scholar
• Vakayil, A., and Joseph, V. R. (2022), “Data Twinning,” Statistical Analysis and Data Mining: The ASA Data Science Journal, 15, 598–610. DOI: 10.1002/sam.11574.
   Web of Science ®Google Scholar
• Vencálek, O. (2017), “Depth-based Classification for Multivariate Data,” Austrian Journal of Statistics, 46, 117–128. DOI: 10.17713/ajs.v46i3-4.677.
   Google Scholar
• Villa-Pérez, M. E., Alvarez-Carmona, M. A., Loyola-Gonzalez, O., Medina-Pérez, M. A., Velazco-Rossell, J. C., and Choo, K.-K. R. (2021), “Semi-Supervised Anomaly Detection Algorithms: A Comparative Summary and Future Research Directions,” Knowledge-Based Systems, 218, 106878. DOI: 10.1016/j.knosys.2021.106878.
   Web of Science ®Google Scholar
• Wang, H., and Xia, Y. (2008), “Sliced Regression for Dimension Reduction,” Journal of the American Statistical Association, 103, 811–821. DOI: 10.1198/016214508000000418.
   Web of Science ®Google Scholar
• Wang, X., Wang, Z., Shao, W., Jia, C., and Li, X. (2019), “Explaining Concept Drift of Deep Learning Models,” in International Symposium on Cyberspace Safety and Security, pp. 524–534, Springer.
   Google Scholar
• Weese, M., Martinez, W., Megahed, F. M., and Jones-Farmer, L. A. (2016), “Statistical Learning Methods Applied to Process Monitoring: An Overview and Perspective,” Journal of Quality Technology, 48, 4–24. DOI: 10.1080/00224065.2016.11918148.
   Web of Science ®Google Scholar
• Wu, H.-M. (2008), “Kernel Sliced Inverse Regression with Applications to Classification,” Journal of Computational and Graphical Statistics, 17, 590–610. DOI: 10.1198/106186008X345161.
   Web of Science ®Google Scholar
• Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., Sun, Y., et al. (2022a), “OpenOOD: Benchmarking Generalized Out-of-Distribution Detection,” arXiv preprint arXiv:2210.07242.
   Google Scholar
• Yang, J., Zhou, K., Li, Y., and Liu, Z. (2022b), “Generalized Out-of-Distribution Detection: A Survey,” arXiv preprint arXiv:2110.11334v2.
   Google Scholar
• Yeganeh, A., Abbasi, S. A., Pourpanah, F., Shadman, A., Johannssen, A., and Chukhrova, N. (2022), “An Ensemble Neural Network Framework for Improving the Detection Ability of a Base Control Chart in Non-parametric Profile Monitoring,” Expert Systems with Applications, 204, 117572. DOI: 10.1016/j.eswa.2022.117572.
   Web of Science ®Google Scholar
• Yeung, D.-Y., and Chow, C. (2002), “Parzen-Window Network Intrusion Detectors,” in 2002 International Conference on Pattern Recognition (Vol. 4), pp. 385–388, IEEE.
   Google Scholar
• Yeung, D.-Y., and Ding, Y. (2003), “Host-based Intrusion Detection Using Dynamic and Static Behavioral Models,” Pattern Recognition, 36, 229–243. DOI: 10.1016/S0031-3203(02)00026-2.
   Web of Science ®Google Scholar
• Yu, F., Wang, D., Shelhamer, E., and Darrell, T. (2018), “Deep Layer Aggregation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412.
   Google Scholar
• Zan, T., Liu, Z., Su, Z., Wang, M., Gao, X., and Chen, D. (2020), “Statistical Process Control with Intelligence based on the Deep Learning Model,” Applied Sciences, 10, 308. DOI: 10.3390/app10010308.
   Google Scholar
• Zhang, K., Bui, A. T., and Apley, D. W. (2023), “Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors,” Technometrics, 65, 137–149. DOI: 10.1080/00401706.2022.2124310.
   Web of Science ®Google Scholar
• Žliobaitė, I., Pechenizkiy, M., and Gama, J. (2016), “An Overview of Concept Drift Applications,” in Big Data Analysis: New Algorithms for a New Society, eds. N. Japkowicz, and J. Stefanowski, 91–114, Cham: Springer.
   Google Scholar
• Zou, C., Wang, Z., and Tsung, F. (2012), “A Spatial Rank-based Multivariate EWMA Control Chart,” Naval Research Logistics (NRL), 59, 91–110. DOI: 10.1002/nav.21475.
   Web of Science ®Google Scholar
• Zuo, Y., and Serfling, R. (2000), “General Notions of Statistical Depth Function,” Annals of Statistics, 28, 461–482.
   Web of Science ®Google Scholar