{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:05:00Z","timestamp":1780463100855,"version":"3.54.1"},"reference-count":67,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100016221","name":"Politehnica University of Bucharest","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100016221","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Image binarization is a preprocessing technique that maps an image\u2019s pixel values to either black or white, and it is crucial in many fields of computer vision, such as document digitization and medical imaging. Thresholding is a popular image binarization technique for grayscale images because it splits pixel values into greater than or lower than a specific threshold. Global thresholding is fast because it computes only one threshold for the entire image, but it cannot handle many types of noise specific to document images. Local thresholding has greater computational complexity because it adjusts the thresholds for each pixel based on the surrounding pixels, but it can handle such types of noise, although it risks introducing noise in uniform areas of the image. Mixed global\u2013local approaches can mitigate this risk while still being able to handle most types of noise. This paper proposes a mixed global\u2013local thresholding method that harnesses two popular automatic machine learning frameworks to train machine learning models using the results of several thresholding algorithms and other image statistics. Cross-validation was performed to ensure that the selected models are robust and perform well on new data. We obtained results comparable with other state-of-the-art methods on popular document image binarization datasets.<\/jats:p>","DOI":"10.3390\/make8060149","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T08:09:16Z","timestamp":1780301356000},"page":"149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Document Image Binarization Using Various Machine Learning Models and Ensembles Trained on Classic Local and Global Binarization Algorithms and Image Statistics"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7769-8289","authenticated-orcid":false,"given":"Nicolae","family":"Tarb\u0103","sequence":"first","affiliation":[{"name":"Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2987-4022","authenticated-orcid":false,"given":"Costin-Anton","family":"Boiangiu","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6764-9125","authenticated-orcid":false,"given":"Mihai-Lucian","family":"Voncil\u0103","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Binarization Methods in Multimedia Systems when Recognizing License Plates of Cars","volume":"7","author":"Tahseen","year":"2023","journal-title":"Int. J. Acad. Eng. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8096","DOI":"10.3390\/app15148096","article-title":"From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning","volume":"15","author":"Boiangiu","year":"2025","journal-title":"Appl. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tarb\u0103, N., Boiangiu, C.A., and Voncil\u0103, M.L. (2025). State-of-the-art document image binarization using a decision tree ensemble trained on classic local binarization algorithms and image statistics. Appl. Sci., 15.","DOI":"10.3390\/app15158374"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gatos, B., Ntirogiannis, K., and Pratikakis, I. (2009). ICDAR 2009 Document Image Binarization Contest (DIBCO 2009). 2009 10th International Conference on Document Analysis and Recognition, IEEE.","DOI":"10.1109\/ICDAR.2009.246"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Gatos, B., and Ntirogiannis, K. (2010). H-DIBCO 2010\u2014Handwritten Document Image Binarization Competition. 2010 12th International Conference on Frontiers in Handwriting Recognition, IEEE.","DOI":"10.1109\/ICFHR.2010.118"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Gatos, B., and Ntirogiannis, K. (2011). ICDAR 2011 Document Image Binarization Contest (DIBCO 2011). International Conference on Document Analysis and Recognition, IEEE.","DOI":"10.1109\/ICDAR.2011.299"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Gatos, B., and Ntirogiannis, K. (2012). ICFHR 2012 Competition on Handwritten Document Image Binarization (H-DIBCO 2012). International Conference on Frontiers in Handwriting Recognition, IEEE.","DOI":"10.1109\/ICFHR.2012.216"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Gatos, B., and Ntirogiannis, K. (2013). ICDAR 2013 Document Image Binarization Contest (DIBCO 2013). 2013 12th International Conference on Document Analysis and Recognition, IEEE.","DOI":"10.1109\/ICDAR.2013.219"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ntirogiannis, K., Gatos, B., and Pratikakis, I. (2014). ICFHR2014 Competition on Handwritten Document Image Binarization (H-DIBCO 2014). 2014 14th International Conference on Frontiers in Handwriting Recognition, IEEE.","DOI":"10.1109\/ICFHR.2014.141"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Zagoris, K., Barlas, G., and Gatos, B. (2016). ICFHR2016 Handwritten Document Image Binarization Contest (H-DIBCO 2016). 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE.","DOI":"10.1109\/ICFHR.2016.0118"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Zagoris, K., Barlas, G., and Gatos, B. (2017). ICDAR2017 Competition on Document Image Binarization (DIBCO 2017). 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), IEEE.","DOI":"10.1109\/ICDAR.2017.228"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Zagoris, K., Kaddas, P., and Gatos, B. (2018). ICFHR 2018 Competition on Handwritten Document Image Binarization (H-DIBCO 2018). 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR), IEEE.","DOI":"10.1109\/ICFHR-2018.2018.00091"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pratikakis, I., Zagoris, K., Karagiannis, X., Tsochatzidis, L., Mondal, T., and Marthot-Santaniello, I. (2019). ICDAR 2019 Competition on Document Image Binarization (DIBCO 2019). 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE.","DOI":"10.1109\/ICDAR.2019.00249"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1016\/j.patcog.2009.12.024","article-title":"A Multi-Scale Framework for Adaptive Binarization of Degraded Document Images","volume":"43","author":"Moghaddam","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1016\/j.patcog.2011.12.013","article-title":"AdOtsu: An Adaptive and Parameterless Generalization of Otsu\u2019s Method for Document Image Binarization","volume":"45","author":"Moghaddam","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bloechle, J.-L., Hennebert, J., and Gisler, C. (2023). YinYang, a Fast and Robust Adaptive Document Image Binarization for Optical Character Recognition. DocEng \u203223: Proceedings of the ACM Symposium on Document Engineering, ACM.","DOI":"10.1145\/3573128.3609354"},{"key":"ref_18","first-page":"1","article-title":"ZigZag: A Robust Adaptive Approach to Non-Uniformly Illuminated Document Image Binarization","volume":"Volume 17","author":"Bloechle","year":"2024","journal-title":"ACM Symposium on Document Engineering 2024"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10032-012-0192-x","article-title":"Document Binarization with Automatic Parameter Tuning","volume":"16","author":"Howe","year":"2012","journal-title":"Int. J. Doc. Anal. Recognit. (IJDAR)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.patcog.2017.09.032","article-title":"Degraded Document Image Binarization Using Structural Symmetry of Strokes","volume":"74","author":"Jia","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_21","first-page":"37","article-title":"A Selectional Auto-Encoder Approach for Document Image Binarization","volume":"86","author":"Gallego","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.patcog.2019.01.025","article-title":"DeepOtsu: Document Enhancement and Binarization Using Iterative Deep Learning","volume":"91","author":"He","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108010","DOI":"10.1016\/j.patcog.2021.108010","article-title":"CT-Net: Cascade T-Shape Deep Fusion Networks for Document Binarization","volume":"118","author":"He","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Peng, X., Wang, C., and Cao, H. (2019). Document Binarization via Multi-Resolutional Attention Model with DRD Loss. 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE.","DOI":"10.1109\/ICDAR.2019.00017"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mondal, R., Chakraborty, D., and Chanda, B. (2019). Learning 2D Morphological Network for Old Document Image Binarization. 2019 International Conference on Document Analysis and Recognition (ICDAR), IEEE.","DOI":"10.1109\/ICDAR.2019.00020"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/LSP.2020.3003828","article-title":"Document Image Binarization Using Dual Discriminator Generative Adversarial Networks","volume":"27","author":"De","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106968","DOI":"10.1016\/j.patcog.2019.106968","article-title":"Document Image Binarization with Cascaded Generators of Conditional Generative Adversarial Networks","volume":"96","author":"Zhao","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1109\/TPAMI.2020.3022406","article-title":"DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement","volume":"44","author":"Souibgui","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108810","DOI":"10.1016\/j.patcog.2022.108810","article-title":"Two-Stage Generative Adversarial Networks for Binarization of Color Document Images","volume":"130","author":"Suh","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.inffus.2022.12.011","article-title":"A Novel Degraded Document Binarization Model through Vision Transformer Network","volume":"93","author":"Yang","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_31","unstructured":"Biswas, R., Roy, S.K., Wang, N., Pal, U., and Huang, G.B. (2023). DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109989","DOI":"10.1016\/j.patcog.2023.109989","article-title":"GDB: Gated Convolutions-Based Document Binarization","volume":"146","author":"Yang","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Siddiqui, F.U., and Yahya, A. (2021). Clustering Techniques for Image Segmentation, Springer Nature.","DOI":"10.1007\/978-3-030-81230-0"},{"key":"ref_34","first-page":"90240R","article-title":"Image Thresholding Using Standard Deviation","volume":"Volume 9024","author":"Sung","year":"2014","journal-title":"Proceedings of SPIE 9024, Image Processing: Machine Vision Applications VII"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/0031-3203(93)90115-D","article-title":"Minimum Cross Entropy Thresholding","volume":"26","author":"Li","year":"1993","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1007\/s11831-020-09416-2","article-title":"A Survey on Global Thresholding Methods for Mapping Open Water Body Using Sentinel-2 Satellite Imagery and Normalized Difference Water Index","volume":"28","author":"Sekertekin","year":"2020","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0031-3203(95)00066-6","article-title":"Minimum Cross-Entropy Threshold Selection","volume":"29","author":"Brink","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_38","unstructured":"Bernsen, J. (1986). Dynamic Thresholding of Grey-Level Images. 8th International Conference on Pattern Recognition (ICPR), IEEE."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1147\/rd.274.0400","article-title":"Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction","volume":"27","author":"White","year":"1983","journal-title":"IBM J. Res. Dev."},{"key":"ref_40","unstructured":"Niblack, W. (1986). An Introduction to Digital Image Processing, ACM."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Khurshid, K., Siddiqi, I., Faure, C., and Vincent, N. (2008). Comparison of Niblack Inspired Binarization Methods for Ancient Documents. Proceedings of SPIE, the International Society for Optical Engineering, SPIE.","DOI":"10.1117\/12.805827"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/S0031-3203(99)00055-2","article-title":"Adaptive Document Image Binarization","volume":"33","author":"Sauvola","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s10044-003-0197-7","article-title":"Extraction and Recognition of Artificial Text in Multimedia Documents","volume":"6","author":"Wolf","year":"2004","journal-title":"Pattern Anal. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Phansalkar, N.N., More, N.S., Sabale, N.A., and Joshi, N.M. (2011). Adaptive Local Thresholding for Detection of Nuclei in Diversity Stained Cytology Images. 2011 International Conference on Communications and Signal Processing, IEEE.","DOI":"10.1109\/ICCSP.2011.5739305"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"8","DOI":"10.22237\/jmasm\/1177992120","article-title":"Bimodality revisited","volume":"6","author":"Knapp","year":"2007","journal-title":"J. Mod. Appl. Stat. Methods"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tarb\u0103, N., Voncil\u0103, M.-L., and Boiangiu, C.-A. (2022). On Generalizing Sarle\u2019s Bimodality Coefficient as a Path towards a Newly Composite Bimodality Coefficient. Mathematics, 10.","DOI":"10.3390\/math10071042"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ahmed, Z., Amizadeh, S., Bilenko, M., Carr, R., Chin, W.-S., Dekel, Y., Dupre, X., Eksarevskiy, V., Filipi, S., and Finley, T. (2019). Machine Learning at Microsoft with ML.NET. KDD \u203219: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM.","DOI":"10.1145\/3292500.3330667"},{"key":"ref_48","unstructured":"LeDell, E., and Poirier, S. (2020, January 18). H2O AutoML: Scalable Automatic Machine Learning. Proceedings of the 7th ICML Workshop on Automated Machine Learning (AutoML), Virtual."},{"key":"ref_49","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 1\u20139). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Berrar, D. (2018). Cross-Validation. Encyclopedia of Bioinformatics and Computational Biology, Elsevier. Elsevier eBooks.","DOI":"10.1016\/B978-0-12-809633-8.20349-X"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"Volume 45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Akhilesh, T.M., Sankar, J., Rani, N.S., and Nair, B.B.J. (2023). Enhancement of Palm Leaf Manuscripts Images Using Deep SegNet Model. 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), IEEE.","DOI":"10.1109\/ICCUBEA58933.2023.10391990"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"62937","DOI":"10.1007\/s11042-023-18020-y","article-title":"A modified deep semantic binarization network for degradation removal in palm leaf manuscripts","volume":"83","author":"Nair","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yuadi, I., Nisa\u2019, K., Nazikhah, N., Halim, Y., Asyhari, A., and Hu, C. (2025). A Benchmark Study of Classical and U-Net ResNet34 Methods for Binarization of Balinese Palm Leaf Manuscripts. Heritage, 8.","DOI":"10.3390\/heritage8080337"},{"key":"ref_55","unstructured":"Groleau, A., Chee, K., Larson, S., Maini, S., and Boarman, J. (2023). ShabbyPages: A Reproducible Document Denoising and Binarization Dataset. arXiv."},{"key":"ref_56","unstructured":"Ju, R., Yamashita, K., Kameko, H., and Mori, S. (2025). DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"126453","DOI":"10.1016\/j.eswa.2025.126453","article-title":"SDADDS-Guelma: A large-scale, multi-purpose dataset for degraded Arabic document analysis and recognition","volume":"269","author":"Kefali","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guo, H., Tong, L., Zhao, J., Guan, L., Jiang, A., and Gao, Y. (2026). SEADUNet: A Multilingual Ancient Document Image Binarization using EMCAM Attention Mechanism and SCP. ACM Transactions on Multimedia Computing, Communications and Applications, ACM.","DOI":"10.1145\/3800946"},{"key":"ref_59","unstructured":"Jena, P., Joseph, A., Sharma, A., and Sarvadevabhatla, R. (2026). Unveiling Text in Challenging Stone Inscriptions: A Character-Context-Aware Patching Strategy for Binarization. arXiv."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Maheshwari, Y., Guha, D., Kuiry, S., and Das, N. (2025). YAM: A Novel Binarization Approach for Non-Uniformly Illuminated Document Images. 2025 Seventh International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), IEEE.","DOI":"10.1109\/ICRCICN68210.2025.11364953"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Haupts, P., Al-Joumaa, H., Al-Shrouf, L., and Jelali, M. (2025). Comparative Analysis of Binarization Approaches for Automated Dye Penetrant Testing. Processes, 13.","DOI":"10.3390\/pr13041212"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"12648","DOI":"10.1038\/s41598-026-38494-8","article-title":"A hybrid spatial blur detection and restoration algorithm for smartphone captured document images","volume":"16","author":"Karthik","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"19655","DOI":"10.1038\/s41598-023-46785-7","article-title":"Features extraction from multi-spectral remote sensing images based on multi-threshold binarization","volume":"13","author":"Rusyn","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"627","DOI":"10.18287\/2412-6179-CO-1207","article-title":"A joint study of deep learning-based methods for identity document image binarization and its influence on attribute recognition","volume":"47","author":"Bezmaternykh","year":"2023","journal-title":"Comput. Opt."},{"key":"ref_65","first-page":"838","article-title":"Unsupervised Document Binarization of Engineering Drawings via Multi Noise CycleGAN","volume":"14","author":"Rosli","year":"2023","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ntirogiannis, K., Gatos, B., and Pratikakis, I. (2008). An Objective Evaluation Methodology for Document Image Binarization Techniques. 2008 The Eighth IAPR International Workshop on Document Analysis Systems, IEEE.","DOI":"10.1109\/DAS.2008.41"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1080\/01621459.2023.2197686","article-title":"Cross-Validation: What Does It Estimate and How Well Does It Do It?","volume":"119","author":"Bates","year":"2023","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/6\/149\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:19:55Z","timestamp":1780460395000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/8\/6\/149"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":67,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["make8060149"],"URL":"https:\/\/doi.org\/10.3390\/make8060149","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}