{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:51:19Z","timestamp":1777107079065,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","funder":[{"name":"Bayerisches Staatsministerium f\u00fcr Wirtschaft, Landesentwicklung und Energie","award":["0703\/68362\/298\/21\/16\/22\/17\/23\/18\/24"],"award-info":[{"award-number":["0703\/68362\/298\/21\/16\/22\/17\/23\/18\/24"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,14]]},"DOI":"10.1145\/3787279.3787282","type":"proceedings-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:38:47Z","timestamp":1777102727000},"page":"11-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of Bagging Predictors with Kernel Density Estimation and Bagging Score"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1096-1721","authenticated-orcid":false,"given":"Philipp","family":"Seitz","sequence":"first","affiliation":[{"name":"Institute of Digital Engineering (IDEE), Technical University of Applied Sciences W\u00fcrzburg-Schweinfurt (THWS), Schweinfurt, Bavaria, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4537-7680","authenticated-orcid":false,"given":"Jan","family":"Schmitt","sequence":"additional","affiliation":[{"name":"Institute of Digital Engineering (IDEE), Technical University of Applied Sciences W\u00fcrzburg-Schweinfurt (THWS), Schweinfurt, Bavaria, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1447-7331","authenticated-orcid":false,"given":"Andreas","family":"Schiffler","sequence":"additional","affiliation":[{"name":"Institute of Digital Engineering (IDEE), Technical University of Applied Sciences W\u00fcrzburg-Schweinfurt (THWS), Schweinfurt, Bavaria, Germany"}]}],"member":"320","published-online":{"date-parts":[[2026,4,25]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Hunter David Hao Yu Michael S Pukish III Janusz Kolbusz and Bogdan M Wilamowski. 2012. \u201cSelection of proper neural network sizes and architectures\u2014A comparative study.\u201d IEEE Transactions on Industrial Informatics 8 (2): 228\u2013240.","DOI":"10.1109\/TII.2012.2187914"},{"key":"e_1_3_3_1_3_2","unstructured":"Sykes Alan O. 1993. \u201cAn introduction to regression analysis.\u201d."},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Breiman Leo. 1996. \u201cBagging predictors.\u201d Machine learning 24: 123\u2013140.","DOI":"10.1023\/A:1018054314350"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Schapire Robert E. 2003. \u201cThe boosting approach to machine learning: An overview.\u201d Nonlinear estimation and classification 149\u2013171.","DOI":"10.1007\/978-0-387-21579-2_9"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Drucker Harris Corinna Cortes Lawrence D Jackel Yann LeCun and Vladimir Vapnik. 1994. \u201cBoosting and other machine learning algorithms.\u201d In Machine Learning Proceedings 1994 53\u201361. Elsevier.","DOI":"10.1016\/B978-1-55860-335-6.50015-5"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Wang Ying Yong Fan Priyanka Bhatt and Christos Davatzikos. 2010. \u201cHigh-dimensional pattern regression using machine learning: from medical images to continuous clinical variables.\u201d Neuroimage 50 (4): 1519\u20131535.","DOI":"10.1016\/j.neuroimage.2009.12.092"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Bauer Eric and Ron Kohavi. 1999. \u201cAn empirical comparison of voting classification algorithms: Bagging boosting and variants.\u201d Machine learning 36: 105\u2013139.","DOI":"10.1023\/A:1007515423169"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Friedman Jerome H and Peter Hall. 2007. \u201cOn bagging and nonlinear estimation.\u201d Journal of statistical planning and inference 137 (3): 669\u2013683.","DOI":"10.1016\/j.jspi.2006.06.002"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Chen Tao and Jianghong Ren. 2009. \u201cBagging for Gaussian process regression.\u201d Neurocomputing 72 (7-9): 1605\u20131610.","DOI":"10.1016\/j.neucom.2008.09.002"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Grandvalet Yves. 2004. \u201cBagging equalizes influence.\u201d Machine Learning 55: 251\u2013270.","DOI":"10.1023\/B:MACH.0000027783.34431.42"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Guo Hongwei Xiaoying Zhuang Jianfeng Chen and Hehua Zhu. 2022. \u201cPredicting earthquake-induced soil liquefaction based on machine learning classifiers: A comparative multi-dataset study.\u201d International Journal of Computational Methods 19 (08): 2142004.","DOI":"10.1142\/S0219876221420044"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Lin Shan Zenglong Liang Shuaixing Zhao Miao Dong Hongwei Guo and Hong Zheng. 2024. \u201cA comprehensive evaluation of ensemble machine learning in geotechnical stability analysis and explainability.\u201d International Journal of Mechanics and Materials in Design 20 (2): 331\u2013352.","DOI":"10.1007\/s10999-023-09679-0"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Parzen Emanuel. 1962. \u201cOn estimation of a probability density function and mode.\u201d The annals of mathematical statistics 33 (3): 1065\u20131076.","DOI":"10.1214\/aoms\/1177704472"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Weglarczyk Stanislaw. 2018. \u201cKernel density estimation and its application.\u201d In ITM web of conferences Vol. 23 00037. EDP Sciences.","DOI":"10.1051\/itmconf\/20182300037"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Seitz Philipp and Jan Schmitt. 2023. \u201cAlternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks.\u201d Journal of Experimental & Theoretical Artificial Intelligence 1\u201322.","DOI":"10.1080\/0952813X.2023.2270995"},{"key":"e_1_3_3_1_17_2","unstructured":"Yeh I-C. 1998. \u201cModeling of strength of high-performance concrete using artificial neural networks.\u201d Accessed: 2023-09-13 https:\/\/www.kaggle.com\/datasets\/maajdl\/yeh-concret-data."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Chen Yen-Chi. 2017. \u201cA tutorial on kernel density estimation and recent advances.\u201d Biostatistics & Epidemiology 1 (1): 161\u2013187.","DOI":"10.1080\/24709360.2017.1396742"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Sheather Simon J. 2004. \u201cDensity estimation.\u201d Statistical science 588\u2013597.","DOI":"10.1214\/088342304000000297"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Mohandoss Divya Pramasani Yong Shi and Kun Suo. 2021. \u201cOutlier prediction using random forest classifier.\u201d In 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC) 0027\u20130033. IEEE.","DOI":"10.1109\/CCWC51732.2021.9376077"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"YEH I.-C. Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete research 1998 28. Jg. Nr. 12 S. 1797-1808.","DOI":"10.1016\/S0008-8846(98)00165-3"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"CHOU Jui-Sheng et al. Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. Journal of Computing in Civil Engineering 2011 25. Jg. Nr. 3 S. 242-253.","DOI":"10.1061\/(ASCE)CP.1943-5487.0000088"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"ERDAL Halil Ibrahim; KARAKURT Onur; NAMLI Ersin. High performance concrete compressive strength forecasting using ensemble models based on discrete wavelet transform. Engineering Applications of Artificial Intelligence 2013 26. Jg. Nr. 4 S. 1246-1254.","DOI":"10.1016\/j.engappai.2012.10.014"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"crossref","unstructured":"CHOU Jui-Sheng; PHAM Anh-Duc. Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength. Construction and Building Materials 2013 49. Jg. S. 554-563.","DOI":"10.1016\/j.conbuildmat.2013.08.078"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"crossref","unstructured":"CHOU Jui-Sheng et al. Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building materials 2014 73. Jg. S. 771-780.","DOI":"10.1016\/j.conbuildmat.2014.09.054"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"crossref","unstructured":"CHENG Min-Yuan; FIRDAUSI Pratama Mahardika; PRAYOGO Doddy. High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT). Engineering Applications of Artificial Intelligence 2014 29. Jg. S. 104-113.","DOI":"10.1016\/j.engappai.2013.11.014"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"crossref","unstructured":"PHAM Anh-Duc; HOANG Nhat-Duc; NGUYEN Quang-Trung. Predicting compressive strength of high-performance concrete using metaheuristic-optimized least squares support vector regression. Journal of Computing in Civil Engineering 2016 30. Jg. Nr. 3 S. 06015002.","DOI":"10.1061\/(ASCE)CP.1943-5487.0000506"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"crossref","unstructured":"HAN Qinghua et al. A generalized method to predict the compressive strength of high-performance concrete by improved random forest algorithm. Construction and Building Materials 2019 226. Jg. S. 734-742.","DOI":"10.1016\/j.conbuildmat.2019.07.315"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"crossref","unstructured":"CHAKRABORTY Debaditya; AWOLUSI Ibukun; GUTIERREZ Lilianna. An explainable machine learning model to predict and elucidate the compressive behavior of high-performance concrete. Results in Engineering 2021 11. Jg. S. 100245.","DOI":"10.1016\/j.rineng.2021.100245"}],"event":{"name":"ICAAI 2025: 2025 9th International Conference on Advances in Artificial Intelligence","location":"Manchester United Kingdom","acronym":"ICAAI 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Advances in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3787279.3787282","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:23:08Z","timestamp":1777105388000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3787279.3787282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":28,"alternative-id":["10.1145\/3787279.3787282","10.1145\/3787279"],"URL":"https:\/\/doi.org\/10.1145\/3787279.3787282","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2026-04-25","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}