{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,14]],"date-time":"2025-06-14T04:03:12Z","timestamp":1749873792536,"version":"3.41.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031953965","type":"print"},{"value":"9783031953972","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-95397-2_14","type":"book-chapter","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T04:23:28Z","timestamp":1749788608000},"page":"227-244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring the\u00a0Influence of\u00a0Data Characteristics on\u00a0Machine Learning Outcomes"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3820-7870","authenticated-orcid":false,"given":"Camilla","family":"Sancricca","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pasquale","family":"Castiglione","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1825-0097","authenticated-orcid":false,"given":"Cinzia","family":"Cappiello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Ajiboye, A., Abdullah-Arshah, R., Hongwu, Q.: Evaluating the effect of dataset size on predictive model using supervised learning technique (2015)","DOI":"10.15282\/ijsecs.1.2015.6.0006"},{"issue":"3","key":"14_CR2","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.11591\/ijeecs.v14.i3.pp1552-1563","volume":"14","author":"H Ali","year":"2019","unstructured":"Ali, H., Salleh, M.M., Saedudin, R., Hussain, K., Mushtaq, M.F.: Imbalance class problems in data mining: a review. Indonesian J. Electric. Eng. Comput. Sci. 14(3), 1560\u20131571 (2019)","journal-title":"Indonesian J. Electric. Eng. Comput. Sci."},{"issue":"2","key":"14_CR3","doi-asserted-by":"publisher","first-page":"796","DOI":"10.3390\/app11020796","volume":"11","author":"A Althnian","year":"2021","unstructured":"Althnian, A., et al.: Impact of dataset size on classification performance: an empirical evaluation in the medical domain. Appl. Sci. 11(2), 796 (2021)","journal-title":"Appl. Sci."},{"key":"14_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106504","volume":"213","author":"A Bailly","year":"2022","unstructured":"Bailly, A., et al.: Effects of dataset size and interactions on the prediction performance of logistic regression and deep learning models. Comput. Methods Programs Biomed. 213, 106504 (2022)","journal-title":"Comput. Methods Programs Biomed."},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41(3), 16:1\u201316:52 (2009)","DOI":"10.1145\/1541880.1541883"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Batini, C., Scannapieco, M.: Data and Information Quality - Dimensions, Principles and Techniques. Data-Centric Systems and Applications. Springer (2016)","DOI":"10.1007\/978-3-319-24106-7"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Chan, J.Y.L., et al.: Mitigating the multicollinearity problem and its machine learning approach: a review. Mathematics 10 (2022)","DOI":"10.3390\/math10081283"},{"issue":"2","key":"14_CR8","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1109\/TR.2021.3070863","volume":"70","author":"H Chen","year":"2021","unstructured":"Chen, H., Chen, J., Ding, J.: Data evaluation and enhancement for quality improvement of machine learning. IEEE Trans. Reliab. 70(2), 831\u2013847 (2021)","journal-title":"IEEE Trans. Reliab."},{"key":"14_CR9","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3389\/frai.2020.00004","volume":"3","author":"F Emmert-Streib","year":"2020","unstructured":"Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., Dehmer, M.: An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3, 4 (2020)","journal-title":"Front. Artif. Intell."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Firmani, D., Tanca, L., Torlone, R.: Ethical dimensions for data quality. ACM J. Data Inf. Qual. 12(1), 2:1\u20132:5 (2020)","DOI":"10.1145\/3362121"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Foroni, D., Lissandrini, M., Velegrakis, Y.: Estimating the extent of the effects of data quality through observations. In: ICDE 2021, pp. 1913\u20131918. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00176"},{"key":"14_CR12","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.procs.2019.11.146","volume":"161","author":"S Gupta","year":"2019","unstructured":"Gupta, S., Gupta, A.: Dealing with noise problem in machine learning data-sets: a systematic review. Procedia Comput. Sci. 161, 466\u2013474 (2019)","journal-title":"Procedia Comput. Sci."},{"issue":"8","key":"14_CR13","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3571724","volume":"66","author":"MH Jarrahi","year":"2023","unstructured":"Jarrahi, M.H., Memariani, A., Guha, S.: The principles of data-centric AI. Commun. ACM 66(8), 84\u201392 (2023)","journal-title":"Commun. ACM"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Li, P., Rao, X., Blase, J., Zhang, Y., Chu, X., Zhang, C.: CleanML: a study for evaluating the impact of data cleaning on ML classification tasks. In: ICDE 2021, pp. 13\u201324. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00009"},{"key":"14_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2022.100911","volume":"29","author":"AR Luca","year":"2022","unstructured":"Luca, A.R., et al.: Impact of quality, type and volume of data used by deep learning models in the analysis of medical images. Inf. Med. Unlocked 29, 100911 (2022)","journal-title":"Inf. Med. Unlocked"},{"key":"14_CR16","unstructured":"Ma, X., et al.: Dimensionality-driven learning with noisy labels. In: ICML. Proceedings of Machine Learning Research, vol.\u00a080, pp. 3361\u20133370. PMLR (2018)"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 115:1\u2013115:35 (2022)","DOI":"10.1145\/3457607"},{"issue":"7","key":"14_CR18","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.is.2003.12.005","volume":"29","author":"F Naumann","year":"2004","unstructured":"Naumann, F., Freytag, J.C., Leser, U.: Completeness of integrated information sources. Inf. Syst. 29(7), 583\u2013615 (2004)","journal-title":"Inf. Syst."},{"issue":"3","key":"14_CR19","doi-asserted-by":"publisher","first-page":"128","DOI":"10.14445\/22312803\/IJCTT-V48P126","volume":"48","author":"F Osisanwo","year":"2017","unstructured":"Osisanwo, F., et al.: Supervised machine learning algorithms: classification and comparison. Int. J. Comput. Trends Technol. (IJCTT) 48(3), 128\u2013138 (2017)","journal-title":"Int. J. Comput. Trends Technol. (IJCTT)"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Patel, H., et al.: Automatic assessment of quality of your data for AI. In: COMAD\/CODS, pp. 354\u2013357. ACM (2022)","DOI":"10.1145\/3493700.3493774"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Patel, H., Guttula, S.C., Gupta, N., Hans, S., Mittal, R.S., Nagalapatti, L.: A data-centric AI framework for automating exploratory data analysis and data quality tasks. ACM J. Data Inf. Qual. 15(4), 44:1\u201344:26 (2023)","DOI":"10.1145\/3603709"},{"key":"14_CR22","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"14_CR23","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1007\/s11390-021-1344-6","volume":"36","author":"Z Qi","year":"2021","unstructured":"Qi, Z., Wang, H., Wang, A.: Impacts of dirty data on classification and clustering models: an experimental evaluation. J. Comput. Sci. Technol. 36(4), 806\u2013821 (2021)","journal-title":"J. Comput. Sci. Technol."},{"key":"14_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/978-3-030-73194-6_6","volume-title":"Database Systems for Advanced Applications","author":"Z Qi","year":"2021","unstructured":"Qi, Z., Wang, H.: Dirty-data impacts on regression models: an experimental evaluation. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12681, pp. 88\u201395. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-73194-6_6"},{"key":"14_CR25","unstructured":"Sancricca, C., Cappiello, C.: Supporting the design of data preparation pipelines. In: Proceedings of SEBD 2022. CEUR Workshop Proceedings, vol.\u00a03194, pp. 149\u2013158. CEUR-WS.org (2022)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Sancricca, C., Siracusa, G., Cappiello, C.: Enhancing data preparation: insights from a time series case study. J. Intell. Inf. Syst., 1\u201328 (2024)","DOI":"10.1007\/s10844-024-00867-8"},{"key":"14_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.newast.2022.101959","volume":"99","author":"S Sen","year":"2023","unstructured":"Sen, S., Singh, K.P., Chakraborty, P.: Dealing with imbalanced regression problem for large dataset using scalable artificial neural network. New Astron. 99, 101959 (2023)","journal-title":"New Astron."},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Singh, D., Singh, B.: Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97(Part B), 105524 (2020)","DOI":"10.1016\/j.asoc.2019.105524"},{"key":"14_CR29","doi-asserted-by":"crossref","unstructured":"Verma, S., Rubin, J.: Fairness definitions explained. In: Brun, Y., Johnson, B., Meliou, A. (eds.) Proceedings of the International Workshop on Software Fairness, FairWare@ICSE 2018, Gothenburg, Sweden, 29 May 2018, pp.\u00a01\u20137. ACM (2018)","DOI":"10.1145\/3194770.3194776"},{"key":"14_CR30","unstructured":"Virtanen, P., et al.: SciPy 1.0 contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods (2020)"},{"key":"14_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106631","volume":"212","author":"P Vuttipittayamongkol","year":"2021","unstructured":"Vuttipittayamongkol, P., Elyan, E., Petrovski, A.: On the class overlap problem in imbalanced data classification. Knowl. Based Syst. 212, 106631 (2021)","journal-title":"Knowl. Based Syst."},{"issue":"4","key":"14_CR32","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1080\/07421222.1996.11518099","volume":"12","author":"RY Wang","year":"1996","unstructured":"Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5\u201333 (1996)","journal-title":"J. Manag. Inf. Syst."},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Zhao, Y., et al.: On the impact of sample duplication in machine-learning-based android malware detection. ACM Trans. Softw. Eng. Methodol. 30(3), 40:1\u201340:38 (2021)","DOI":"10.1145\/3446905"}],"container-title":["Lecture Notes in Business Information Processing","Enterprise, Business-Process and Information Systems Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95397-2_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T04:23:39Z","timestamp":1749788619000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95397-2_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031953965","9783031953972"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95397-2_14","relation":{},"ISSN":["1865-1348","1865-1356"],"issn-type":[{"value":"1865-1348","type":"print"},{"value":"1865-1356","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"14 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMMSAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Evaluation and Modeling Methods for Systems Analysis and Development","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emmsad2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.emmsad.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}