{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:43:04Z","timestamp":1775068984733,"version":"3.50.1"},"reference-count":94,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Dean\u2019s Office of the Allen E. Paulson College of Engineering and Computing at Georgia Southern University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3436603","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T18:19:51Z","timestamp":1722536391000},"page":"105429-105459","source":"Crossref","is-referenced-by-count":6,"title":["Mastering Precision in Pivotal Variables Defining Wine Quality via Incremental Analysis of Baseline Accuracy"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9552-1731","authenticated-orcid":false,"given":"Cemil Emre","family":"Yavas","sequence":"first","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1068-9855","authenticated-orcid":false,"given":"Jongyeop","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3919-8056","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA, USA"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1714728114"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1109\/SERA61261.2024.10685608","article-title":"Exploring flavors through AI: The future of culinary taste prediction","volume-title":"Proc. 22nd IEEE\/ACIS Int. Conf. Softw. Eng., Manage. Appl. (SERA)","author":"Yavas"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04747-3_8"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.procs.2017.12.041","article-title":"Selection of important features and predicting wine quality using machine learning techniques","volume":"125","author":"Gupta","year":"2018","journal-title":"Proc. Comput. Sci."},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ASIANCON55314.2022.9908870"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3480433.3480443"},{"issue":"5","key":"ref7","first-page":"1","article-title":"Identification of appropriate machine learning algorithm to predict wine quality","volume":"5","author":"Korade","year":"2021","journal-title":"Int. J. Sci. Res. Eng. Manag."},{"issue":"1","key":"ref8","article-title":"Machine learning-based predictive modelling for the enhancement of wine quality","volume-title":"Sci. Rep.","volume":"13","author":"Jain","year":"2023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.51583\/IJLTEMAS.2022.11901"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.4236\/ojs.2021.112015"},{"key":"ref11","article-title":"Wine feature importance and quality prediction: A comparative study of machine learning algorithms with unbalanced data","author":"Zaza","year":"2023","journal-title":"arXiv:2310.01584"},{"key":"ref12","volume-title":"Modeling Wine Quality From Physicochemical Properties","author":"Angus","year":"2023"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100261"},{"issue":"2","key":"ref14","first-page":"120","article-title":"Analysis of white wine using machine learning algorithms","volume":"34","author":"Koranga","year":"2024","journal-title":"J. Wine Sci."},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.11591\/ijai.v12.i2.pp747-754"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1684\/1\/012067"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.29027\/IJIRASE.v4.i4.2020.715-721"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.23919\/icact.2019.8702017"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1966\/1\/012021"},{"issue":"1","key":"ref20","doi-asserted-by":"crossref","first-page":"58","DOI":"10.54254\/2755-2721\/32\/20230184","article-title":"Forecasting red wine quality: A comparative examination of machine learning approaches","volume":"32","author":"Zhan","year":"2024","journal-title":"Appl. Comput. Eng."},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1111\/ajgw.12488"},{"issue":"1","key":"ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17221\/438\/2017-CJFS","article-title":"A study on wine sensory evaluation by the statistical analysis method","volume":"38","author":"Hou","year":"2020","journal-title":"Czech J. Food Sci."},{"issue":"1","key":"ref23","doi-asserted-by":"crossref","first-page":"726","DOI":"10.3390\/molecules20010726","article-title":"The role of visible and infrared spectroscopy combined with chemometrics to measure phenolic compounds in grape and wine samples","volume":"20","author":"Cozzolino","year":"2015","journal-title":"Molecules"},{"issue":"1","key":"ref24","first-page":"858","article-title":"The impact of SO2 on wine flavanols and indoles in relation to wine style and age","volume-title":"Sci. Rep.","volume":"8","author":"Arapitsas","year":"2018"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/ffj.3720"},{"issue":"8","key":"ref26","doi-asserted-by":"crossref","first-page":"380","DOI":"10.5539\/jas.v10n8p380","article-title":"Quality of cabernet sauvignon wines determined by the variability of climatic attributes","volume":"10","author":"Stein","year":"2018","journal-title":"J. Agricult. Sci."},{"issue":"1","key":"ref27","doi-asserted-by":"crossref","first-page":"102","DOI":"10.17113\/ftb.62.01.24.8301","article-title":"Causal artificial intelligence models of food quality data","volume":"62","author":"Kurtanjek","year":"2024","journal-title":"Food Technol. Biotechnol."},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1111\/jfbc.12787"},{"issue":"2","key":"ref29","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1590\/1678-457x.16516","article-title":"Quality evaluation of red wines produced from the Isabella and Ives cultivar (Vitis labrusca): Physicochemical parameters, phenolic composition and antioxidant activity","volume":"37","author":"Arcanjo","year":"2017","journal-title":"Food Sci. Technol."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s13197-014-1644-8"},{"issue":"2","key":"ref31","doi-asserted-by":"crossref","first-page":"42","DOI":"10.60084\/ijds.v1i2.95","article-title":"Enhancing the red wine quality classification using ensemble voting classifiers","volume":"1","author":"Supriatna","year":"2023","journal-title":"Infolitika J. Data Sci."},{"key":"ref32","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodres.2019.108945","article-title":"Effect of aroma perception on taste and mouthfeel dimensions of red wines: Correlation of sensory and chemical measurements","volume":"131","author":"S\u00e1enz-Navajas","year":"2020","journal-title":"Food Res. Int."},{"key":"ref33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/8368992","article-title":"Comparative evaluation of physicochemical, antioxidant, and sensory properties of red wine as markers of its quality and authenticity","volume":"2022","author":"Ofoedu","year":"2022","journal-title":"Int. J. Food Sci."},{"key":"ref34","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.talanta.2016.08.057","article-title":"Combination of visible and mid-infrared spectra for the prediction of chemical parameters of wines","volume":"161","author":"Sen","year":"2016","journal-title":"Talanta"},{"key":"ref35","volume-title":"The UCI Machine Learning Repository","author":"Kelly","year":"2024"},{"key":"ref36","volume-title":"Wine Quality","author":"Cortez","year":"2009"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abf4130"},{"key":"ref38","doi-asserted-by":"crossref","DOI":"10.3389\/fams.2021.716044","article-title":"Entanglement-based feature extraction by tensor network machine learning","volume":"7","author":"Liu","year":"2021","journal-title":"Frontiers Appl. Math. Statist."},{"key":"ref39","doi-asserted-by":"crossref","first-page":"124887","DOI":"10.1109\/ACCESS.2021.3110270","article-title":"Explainability of machine learning models for bankruptcy prediction","volume":"9","author":"Park","year":"2021","journal-title":"IEEE Access"},{"issue":"4","key":"ref40","doi-asserted-by":"crossref","DOI":"10.5812\/aapm-127140","article-title":"Prediction of acute kidney injury after cardiac surgery using interpretable machine learning","volume":"12","author":"Ejmalian","year":"2022","journal-title":"Anesthesiol. Pain Med."},{"key":"ref41","first-page":"1","article-title":"Research on parallel support vector machine based on spark big data platform","volume":"2021","author":"Huimin","year":"2021","journal-title":"Sci. Program."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/1068009.1068307"},{"issue":"2","key":"ref43","doi-asserted-by":"crossref","first-page":"173","DOI":"10.30595\/juita.v10i2.14575","article-title":"Analysis of machine learning algorithm for sleep apnea detection based on heart rate variability","volume":"10","author":"Zakariyah","year":"2022","journal-title":"Jurnal Informatika"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-011-0559-3"},{"issue":"10","key":"ref45","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.2320\/matertrans.MT-MB2022009","article-title":"Machine learning prediction for cementite precipitation in austenite of low-alloy steels","volume":"63","author":"Jeon","year":"2022","journal-title":"Mater. Trans."},{"issue":"16","key":"ref46","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.3390\/app9163322","article-title":"Special issue on using machine learning algorithms in the prediction of kyphosis disease: A comparative study","volume":"9","author":"Dankwa","year":"2019","journal-title":"Appl. Sci."},{"key":"ref47","doi-asserted-by":"crossref","DOI":"10.5194\/amt-2019-308","volume-title":"Gradient Boosting Machine Learning to Improve Satellite-Derived Column Water Vapor Measurement Error","author":"Just","year":"2019"},{"key":"ref48","volume-title":"A Post-Method Condition Analysis of Using Ensemble Machine Learning for Cancer Prognosis and Diagnosis: A Systematic Review","author":"Kavousi","year":"2019"},{"key":"ref49","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2021.709008","article-title":"Corn yield prediction with ensemble CNN-DNN","volume":"12","author":"Shahhosseini","year":"2021","journal-title":"Frontiers Plant Sci."},{"issue":"12","key":"ref50","doi-asserted-by":"crossref","first-page":"4132","DOI":"10.3390\/s18124132","article-title":"Classification of human daily activities using ensemble methods based on smartphone inertial sensors","volume":"18","author":"Rahim","year":"2018","journal-title":"Sensors"},{"key":"ref51","doi-asserted-by":"crossref","first-page":"189","DOI":"10.2147\/RMHP.S225762","article-title":"Machine learning for tuning, selection, and ensemble of multiple risk scores for predicting type 2 diabetes","volume":"12","author":"Liu","year":"2019","journal-title":"Risk Manage. Healthcare Policy"},{"issue":"2","key":"ref52","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1109\/TKDE.2014.2304474","article-title":"Active learning through adaptive heterogeneous ensembling","volume":"27","author":"Lu","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"ref53","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1504\/IJDMB.2016.074876","article-title":"A mixture-of-experts approach for gene regulatory network inference","volume":"14","author":"Shao","year":"2016","journal-title":"Int. J. Data Mining Bioinf."},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.19127\/mbsjohs.889492"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-020-07237-y"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14300"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/acb316"},{"key":"ref58","first-page":"73","article-title":"A non-deterministic strategy for searching optimal number of trees hyperparameter in random forest","volume-title":"Proc. Federated Conf. Comput. Sci. Inf. Syst. (FedCSIS)","author":"Senagi"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0300296"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.4236\/jcc.2024.123010"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.2339\/politeknik.1243881"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3449726.3459532"},{"issue":"5","key":"ref63","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/fi14050153","article-title":"Medical Internet-of-Things based breast cancer diagnosis using hyperparameter-optimized neural networks","volume":"14","author":"Ogundokun","year":"2022","journal-title":"Future Internet"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.202300135"},{"key":"ref65","first-page":"233","article-title":"The relationship between precision-recall and ROC curves","volume-title":"Proc. 23rd Int. Conf. Mach. Learn.","author":"Davis"},{"issue":"3","key":"ref66","first-page":"35","article-title":"Evaluation of information retrieval systems","volume":"4","author":"Zuva","year":"2012","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"issue":"4","key":"ref67","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1111\/2041-210X.13140","article-title":"The area under the precision-recall curve as a performance metric for rare binary events","volume":"10","author":"Sofaer","year":"2019","journal-title":"Methods Ecol. Evol."},{"issue":"1","key":"ref68","first-page":"6439","article-title":"Development and assessment of novel machine learning models to predict the probability of postoperative nausea and vomiting for patient-controlled analgesia","volume-title":"Sci. Rep.","volume":"13","author":"Xie","year":"2023"},{"issue":"5","key":"ref69","doi-asserted-by":"crossref","first-page":"2083","DOI":"10.3390\/app11052083","article-title":"Ischemic stroke prediction by exploring sleep related features","volume":"11","author":"Xie","year":"2021","journal-title":"Appl. Sci."},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2024.30.2.140"},{"issue":"11","key":"ref71","doi-asserted-by":"crossref","first-page":"3540","DOI":"10.1016\/j.jspi.2010.05.026","article-title":"Compare diagnostic tests using transformation-invariant smoothed ROC curves","volume":"140","author":"Tang","year":"2010","journal-title":"J. Stat. Planning Inference"},{"issue":"2","key":"ref72","doi-asserted-by":"crossref","first-page":"55","DOI":"10.32614\/RJ-2018-043","article-title":"nsROC: An R package for non-standard ROC curve analysis","volume":"10","author":"P\u00e9rez-Fern\u00e1ndez","year":"2019","journal-title":"R J."},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1177\/0272989x9401400408"},{"issue":"12","key":"ref74","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1016\/j.patcog.2013.06.014","article-title":"ROC curves for regression","volume":"46","author":"Hern\u00e1ndez-Orallo","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5386"},{"key":"ref76","first-page":"2166","article-title":"Data fusion for outlier detection through pseudo-ROC curves and rank distributions","volume-title":"Proc. IEEE Int. Joint Conf. Neural Netw.","author":"Evangelista"},{"key":"ref77","article-title":"A deep-learning model with the attention mechanism could rigorously predict survivals in neuroblastoma","volume":"11","author":"Feng","year":"2021","journal-title":"Frontiers Oncol."},{"issue":"1","key":"ref78","first-page":"89","article-title":"ROC curve, lift chart and calibration plot","volume":"3","author":"Vuk","year":"2006","journal-title":"Adv. Methodol. Statist."},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-008-9181-z"},{"issue":"14","key":"ref80","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1088\/0031-9155\/51\/14\/013","article-title":"ROC curves predicted by a model of visual search","volume":"51","author":"Chakraborty","year":"2006","journal-title":"Phys. Med. Biol."},{"issue":"14","key":"ref81","doi-asserted-by":"crossref","first-page":"2832","DOI":"10.3390\/nu14142832","article-title":"Development and validation of an insulin resistance model for a population with chronic kidney disease using a machine learning approach","volume":"14","author":"Lee","year":"2022","journal-title":"Nutrients"},{"issue":"1","key":"ref82","doi-asserted-by":"crossref","first-page":"12","DOI":"10.3390\/metabo13010012","article-title":"Comparison of the three most commonly used metabolic syndrome definitions in the Chinese population: A prospective study","volume":"13","author":"Huang","year":"2022","journal-title":"Metabolites"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1111\/jfpp.14999"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1111\/sdi.13191"},{"key":"ref85","article-title":"A cuproptosis random forest cox score model-based evaluation of prognosis, mutation characterization, immune infiltration, and drug sensitivity in hepatocellular carcinoma","volume":"14","author":"Liu","year":"2023","journal-title":"Frontiers Immunol."},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1051\/e3sconf\/202125702080"},{"issue":"7","key":"ref87","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.3390\/w12072057","article-title":"Debris flow susceptibility assessment using the integrated random forest based steady-state infinite slope method: A case study in Changbai mountain, China","volume":"12","author":"Si","year":"2020","journal-title":"Water"},{"issue":"7","key":"ref88","first-page":"2389","article-title":"Balancing interpretability and performance: Optimizing random forest algorithm based on point-to-point federated learning","volume":"20","author":"Gao","year":"2024","journal-title":"J. Electr. Syst."},{"issue":"1","key":"ref89","first-page":"154","article-title":"Implied volatility forecasting for American options based on random forest regressor, linear regression model","volume":"85","author":"Liu","year":"2024","journal-title":"Adv. Econ., Manage. Political Sci."},{"issue":"1","key":"ref90","doi-asserted-by":"crossref","first-page":"32","DOI":"10.2202\/1544-6115.1691","article-title":"Random forests for genetic association studies","volume":"10","author":"Goldstein","year":"2011","journal-title":"Stat. Appl. Genet. Mol. Biol."},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.0c00454"},{"key":"ref92","first-page":"635","article-title":"Prediction of thermal insulation performance of vacuum glass based on extreme random forest model","volume-title":"Proc. SPIE","volume":"12287","author":"Liu"},{"issue":"1","key":"ref93","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1186\/s12911-023-02306-0","article-title":"Machine learning clinical prediction models for acute kidney injury: The impact of baseline creatinine on prediction efficacy","volume":"23","author":"Rahimi","year":"2023","journal-title":"BMC Med. Informat. Decis. Making"},{"issue":"5","key":"ref94","doi-asserted-by":"crossref","first-page":"251","DOI":"10.14740\/jocmr5167","article-title":"Predicting dropout from cognitive behavioral therapy for panic disorder using machine learning algorithms","volume":"16","author":"Ogawa","year":"2024","journal-title":"J. Clin. Med. Res."}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10620272.pdf?arnumber=10620272","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T15:54:16Z","timestamp":1732550056000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10620272\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":94,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3436603","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}