{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T03:14:53Z","timestamp":1785726893984,"version":"3.56.0"},"reference-count":76,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss functions within the eXtreme Gradient Boosting (XGBoost) algorithm, which is a critical step in enhancing the algorithm\u2019s performance for specific applications. Our research is motivated by the need for precision and efficiency in the industrial domain, where the implications of misclassification can be substantial. We focus on the drill-wear analysis of melamine-faced chipboard, a common material in furniture production, to demonstrate the impact of custom loss functions. The paper explores several variants of Weighted Softmax Loss Functions, including Edge Penalty and Adaptive Weighted Softmax Loss, to address the challenges of class imbalance and the heightened importance of accurately classifying edge classes. Our findings reveal that these custom loss functions significantly reduce critical errors in classification without compromising the overall accuracy of the model. This research not only contributes to the field of industrial machine learning by providing a nuanced approach to loss function customization but also underscores the importance of context-specific adaptations in machine learning algorithms. The results showcase the potential of tailored loss functions in balancing precision and efficiency, ensuring reliable and effective machine learning solutions in industrial settings.<\/jats:p>","DOI":"10.3390\/s24041092","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T11:03:14Z","timestamp":1707303794000},"page":"1092","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Custom Loss Functions in XGBoost Algorithm for Enhanced Critical Error Mitigation in Drill-Wear Analysis of Melamine-Faced Chipboard"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1567-879X","authenticated-orcid":false,"given":"Micha\u0142","family":"Bukowski","sequence":"first","affiliation":[{"name":"Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2789-4732","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Kurek","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7490-8930","authenticated-orcid":false,"given":"Bartosz","family":"\u015awiderski","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, Warsaw University of Life Sciences, 02-776 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8935-845X","authenticated-orcid":false,"given":"Albina","family":"Jegorowa","sequence":"additional","affiliation":[{"name":"Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences, 02-787 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/S0007-8506(07)60503-4","article-title":"Tool condition monitoring (TCM)\u2014The status of research and industrial application","volume":"44","author":"Byrne","year":"1995","journal-title":"CIRP Ann."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1007\/s00170-014-6738-y","article-title":"Tool condition monitoring (TCM) using neural networks","volume":"78","author":"Liu","year":"2015","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mohamed, A., Hassan, M., M\u2019Saoubi, R., and Attia, H. (2022). Tool condition monitoring for high-performance machining systems\u2014A review. Sensors, 22.","DOI":"10.3390\/s22062206"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"011006","DOI":"10.1115\/1.4055822","article-title":"Indirect Tool Condition Monitoring Using Ensemble Machine Learning Techniques","volume":"145","author":"Schueller","year":"2023","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0043-1648(85)90081-X","article-title":"Monitoring tool wear during wood machining with acoustic emission","volume":"101","author":"Lemaster","year":"1985","journal-title":"Wear"},{"key":"ref_6","first-page":"429","article-title":"Influence of pneumatic pressure on delamination factor of drilling medium density fiberboard","volume":"60","author":"Kun","year":"2015","journal-title":"Wood Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s10086-016-1555-6","article-title":"Effect of tool material on tool wear and delamination during machining of particleboard","volume":"62","author":"Szwajka","year":"2016","journal-title":"J. Wood Sci."},{"key":"ref_8","first-page":"307","article-title":"An examination of the tool life and surface quality during drilling melamine faced chipboard","volume":"62","author":"Szwajka","year":"2017","journal-title":"Wood Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00107-020-01518-9","article-title":"Automated monitoring of delamination factor during up (conventional) and down (climb) milling of melamine-faced MDF using image processing methods","volume":"78","author":"Podziewski","year":"2020","journal-title":"Eur. J. Wood Wood Prod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.15376\/biores.17.3.5349-5371","article-title":"Tool Condition Monitoring for the Chipboard Drilling Process Using Automatic, Signal-based Tool State Evaluation","volume":"17","author":"Swiderski","year":"2022","journal-title":"BioResources"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bukowski, M., Kurek, J., Antoniuk, I., and Jegorowa, A. (2021). Decision Confidence Assessment in Multi-Class Classification. Sensors, 21.","DOI":"10.3390\/s21113834"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/17480272.2021.2005140","article-title":"Methodology evaluation of computer vision small-dimension hole localization","volume":"18","author":"Szymona","year":"2023","journal-title":"Wood Mater. Sci. Eng."},{"key":"ref_13","first-page":"633","article-title":"Developing automatic recognition system of drill wear in standard laminated chipboard drilling process","volume":"64","author":"Osowski","year":"2016","journal-title":"Bull. Pol. Acad. Sci. Tech. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0952-1976(00)00008-7","article-title":"Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network","volume":"13","author":"Kuo","year":"2000","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s00170-011-3504-2","article-title":"Tool condition monitoring based on numerous signal features","volume":"59","author":"Jemielniak","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jmatprotec.2005.10.021","article-title":"Drill wear monitoring using back propagation neural network","volume":"172","author":"Panda","year":"2006","journal-title":"J. Mater. Process. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1007\/s00107-019-01428-5","article-title":"Initial study on the use of support vector machine (SVM) in tool condition monitoring in chipboard drilling","volume":"77","author":"Jegorowa","year":"2019","journal-title":"Eur. J. Wood Wood Prod."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3449","DOI":"10.1109\/LRA.2019.2926666","article-title":"Intelligent machining monitoring using sound signal processed with the wavelet method and a self-organizing neural network","volume":"4","author":"Nasir","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1007\/s00170-021-07325-7","article-title":"A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges","volume":"115","author":"Nasir","year":"2021","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s00226-016-0859-4","article-title":"Tree species recognition system based on macroscopic image analysis","volume":"51","author":"Ibrahim","year":"2017","journal-title":"Wood Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kurek, J., Swiderski, B., Jegorowa, A., Kruk, M., and Osowski, S. (2016, January 29\u201331). Deep learning in assessment of drill condition on the basis of images of drilled holes. Proceedings of the 8th International Conference on Graphic and Image Processing (ICGIP 2016), Tokyo, Japan.","DOI":"10.1117\/12.2266254"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kurek, J., Wieczorek, G., Kruk, B., Jegorowa, A., and Osowski, S. (2017, January 11\u201313). Transfer learning in recognition of drill wear using convolutional neural network. Proceedings of the 18th International Conference on Computational Problems of Electrical Engineering (CPEE), Kutna Hora, Czech Republic.","DOI":"10.1109\/CPEE.2017.8093087"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13","DOI":"10.22630\/MGV.2019.28.1.2","article-title":"Classifiers Ensemble of Transfer Learning for Improved Drill Wear Classification using Convolutional Neural Network","volume":"28","author":"Kurek","year":"2019","journal-title":"Mach. Graph. Vis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3","DOI":"10.22630\/MGV.2019.28.1.1","article-title":"Data Augmentation Techniques for Transfer Learning Improvement in Drill Wear Classification Using Convolutional Neural Network","volume":"28","author":"Kurek","year":"2019","journal-title":"Mach. Graph. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wieczorek, G., Chlebus, M., Gajda, J., Chyrowicz, K., Kontna, K., Korycki, M., Jegorowa, A., and Kruk, M. (2021). Multiclass image classification using gans and cnn based on holes drilled in laminated chipboard. Sensors, 21.","DOI":"10.3390\/s21238077"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s00226-020-01245-7","article-title":"Deep learning methods for drill wear classification based on images of holes drilled in melamine faced chipboard","volume":"55","author":"Jegorowa","year":"2021","journal-title":"Wood Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Jegorowa, A., Antoniuk, I., Kurek, J., Bukowski, M., Do\u0142owa, W., and Czarniak, P. (2020). Time-efficient approach to drill condition monitoring based on images of holes drilled in melamine faced chipboard. BioResources, 15.","DOI":"10.15376\/biores.15.4.9611-9624"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kurek, J., Antoniuk, I., \u015awiderski, B., Jegorowa, A., and Bukowski, M. (2020). Application of Siamese Networks to the Recognition of the Drill Wear State Based on Images of Drilled Holes. Sensors, 20.","DOI":"10.3390\/s20236978"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1007\/s00521-009-0316-5","article-title":"Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor","volume":"19","author":"Kurek","year":"2010","journal-title":"Neural Comput. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jegorowa, A., Kurek, J., Kruk, M., and G\u00f3rski, J. (2022). The Use of Multilayer Perceptron (MLP) to Reduce Delamination during Drilling into Melamine Faced Chipboard. Forests, 13.","DOI":"10.3390\/f13060933"},{"key":"ref_31","first-page":"189","article-title":"Use of nearest neighbors (k-NN) algorithm in tool condition identification in the case of drilling in melamine faced particleboard","volume":"22","author":"Jegorowa","year":"2020","journal-title":"Maderas. Cienc. Y Tecnol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kurek, J. (2018, January 21\u201324). Hybrid Approach Towards the Assessment of a Drill Condition Using Deep Learning and the Support Vector Machine. Proceedings of the 22nd International Computer Science and Engineering Conference (ICSEC), Chiang Mai, Thailand.","DOI":"10.1109\/ICSEC.2018.8712622"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.1080\/10494820.2021.1928235","article-title":"Enhancing the prediction of student performance based on the machine learning XGBoost algorithm","volume":"31","author":"Aammou","year":"2023","journal-title":"Interact. Learn. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s10614-021-10227-1","article-title":"Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering","volume":"61","author":"Stef","year":"2023","journal-title":"Comput. Econ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105242","DOI":"10.1016\/j.cageo.2022.105242","article-title":"Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm","volume":"170","author":"Wang","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.culher.2023.06.024","article-title":"Fire risk level prediction of timber heritage buildings based on entropy and XGBoost","volume":"63","author":"Lei","year":"2023","journal-title":"J. Cult. Herit."},{"key":"ref_37","first-page":"99","article-title":"XGBoost algorithm for orecasting electricity consumption of Germany","volume":"7","author":"Ibrahim","year":"2023","journal-title":"AURUM J. Eng. Syst. Archit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s40537-021-00515-w","article-title":"A Novel Sensitivity-based Method for Feature Selection","volume":"8","author":"Naik","year":"2021","journal-title":"J. Big Data"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"562","DOI":"10.2166\/hydro.2020.098","article-title":"Updating the neural network sediment load models using different sensitivity analysis methods: A regional application","volume":"22","author":"Asheghi","year":"2020","journal-title":"J. Hydroinform."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yeung, D., Cloete, I., Shi, D., and Ng, W. (2010). Sensitivity Analysis for Neural Networks, Springer.","DOI":"10.1007\/978-3-642-02532-7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1007\/s11053-022-10051-w","article-title":"A Novel Approach to Uncertainty Quantification in Groundwater Table Modeling by Automated Predictive Deep Learning","volume":"31","author":"Shan","year":"2022","journal-title":"Nat. Resour. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"106289","DOI":"10.1016\/j.catena.2022.106289","article-title":"A visualized hybrid intelligent model to delineate Swedish fine-grained soil layers using clay sensitivity","volume":"214","author":"Ghaderi","year":"2022","journal-title":"CATENA"},{"key":"ref_43","first-page":"53","article-title":"Characteristic of the wear of a tool coating based on amorphous carbon during chipboard milling","volume":"111","author":"Czarniak","year":"2020","journal-title":"Ann. Wars. Univ. Life Sci. SGGW For. Wood Technol."},{"key":"ref_44","first-page":"54","article-title":"Influence of the microstructure of tool coatings based on Ti and Al on the blunting process during chipboard processing","volume":"112","author":"Czarniak","year":"2020","journal-title":"Ann. Wars. Univ. Life Sci. SGGW For. Wood Technol."},{"key":"ref_45","first-page":"197","article-title":"Condition of edges of particle board laminated after saws on a panel saw","volume":"11","author":"Wieloch","year":"2018","journal-title":"Trieskov\u00e9 A Beztrieskov\u00e9 Obr\u00e1banie Dreva = Chip Chipless Woodwork. Process."},{"key":"ref_46","unstructured":"Pfleiderer (2023, December 17). Meblarstwo i Wyko\u0144czenie Wn\u0119trz. Available online: https:\/\/www.pfleiderer.pl\/en\/produkty\/MEBLARSTWO-I-WYKONCZENIE-WNETRZ\/plyty-laminowane."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.bbe.2017.04.005","article-title":"Ensemble of classifiers and wavelet transformation for improved recognition of Fuhrman grading in clear-cell renal carcinoma","volume":"37","author":"Kruk","year":"2017","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_48","unstructured":"PyWavelets Development Team (2023, December 17). PyWavelets Documentation. Available online: https:\/\/pywavelets.readthedocs.io."},{"key":"ref_49","unstructured":"Grossmann, A., Kronland-Martinet, R., and Morlet, J. (1990). Wavelets, Springer."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.ymssp.2007.06.001","article-title":"An algorithm for the continuous Morlet wavelet transform","volume":"21","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_51","unstructured":"ImageNet Project (2023, December 18). ImageNet. Available online: https:\/\/www.image-net.org."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","unstructured":"Chandola, Y., Virmani, J., Bhadauria, H., and Kumar, P. (2021). Deep Learning for Chest Radiographs, Academic Press."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s10586-017-0921-5","article-title":"Facial expression recognition using histogram of oriented gradients based transformed features","volume":"21","author":"Nazir","year":"2018","journal-title":"Clust. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1016\/j.patrec.2011.01.004","article-title":"Face recognition using Histograms of Oriented Gradients","volume":"32","author":"Bueno","year":"2011","journal-title":"Pattern Recognit. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Jafari, F., and Basu, A. (2023). Saliency-Driven Hand Gesture Recognition Incorporating Histogram of Oriented Gradients (HOG) and Deep Learning. Sensors, 23.","DOI":"10.3390\/s23187790"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7503811","DOI":"10.1109\/TIM.2023.3276530","article-title":"Histogram of Oriented Gradients for Rotor Speed Estimation in Three-Phase Induction Motors","volume":"72","author":"Dias","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"102747","DOI":"10.1016\/j.media.2023.102747","article-title":"Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation","volume":"85","author":"Bhattarai","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). Xgboost: A Scalable Tree Boosting System, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting (With discussion and a rejoinder by the authors)","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Tanveer, M., and Pachori, R.B. (2019). Machine Intelligence and Signal Analysis, Springer.","DOI":"10.1007\/978-981-13-0923-6"},{"key":"ref_64","unstructured":"(2023, December 01). Python API Reference of Xgboost. Available online: https:\/\/xgboost.readthedocs.io\/en\/stable\/python\/python_api.html."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"120596","DOI":"10.1016\/j.eswa.2023.120596","article-title":"Intrusion Detection using hybridized Meta-heuristic techniques with Weighted XGBoost Classifier","volume":"232","author":"Mohiuddin","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/13588265.2022.2075101","article-title":"Prediction of extent of damage in vehicle during crash using improved XGBoost model","volume":"28","author":"Vadhwani","year":"2023","journal-title":"Int. J. Crashworthiness"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Tian, J., Tsai, P.W., Zhang, K., Cai, X., Xiao, H., Yu, K., Zhao, W., and Chen, J. (2023). Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost. IEEE Trans. Artif. Intell., 1\u201313.","DOI":"10.1109\/TAI.2023.3254519"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201916), New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_69","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press. Chapter 6.2.2.3 Softmax Units for Multinoulli Output Distributions."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"121876","DOI":"10.1016\/j.eswa.2023.121876","article-title":"Flexible loss functions for binary classification in gradient-boosted decision trees: An application to credit scoring","volume":"238","author":"Mushava","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_71","unstructured":"Legate, G., Caccia, L., and Belilovsky, E. (2023). Re-weighted softmax cross-entropy to control forgetting in federated learning. arXiv."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.patrec.2020.05.035","article-title":"Imbalance-XGBoost: Leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost","volume":"136","author":"Wang","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"159798","DOI":"10.1016\/j.scitotenv.2022.159798","article-title":"Estimation of the soil arsenic concentration using a geographically weighted XGBoost model based on hyperspectral data","volume":"858","author":"Ye","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_74","first-page":"724","article-title":"A combination of XGBoost and FocalLoss-based cable aging state assessment method","volume":"Volume 12748","author":"Wang","year":"2023","journal-title":"Proceedings of the 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023)"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Fan, C., Li, C., Peng, Y., Shen, Y., Cao, G., and Li, S. (2023). Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost. Electronics, 12.","DOI":"10.3390\/electronics12214442"},{"key":"ref_76","unstructured":"(2024, January 01). GitHub\u2014Dmlc\/Xgboost: Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and More. Runs on Single Machine, Hadoop, Spark, Dask, Flink and DataFlow\u2014github.com. Available online: https:\/\/github.com\/dmlc\/xgboost."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1092\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:56:41Z","timestamp":1760104601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,7]]},"references-count":76,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041092"],"URL":"https:\/\/doi.org\/10.3390\/s24041092","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,7]]}}}