{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:30:38Z","timestamp":1775039438664,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T00:00:00Z","timestamp":1689897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine\u2019s origin.<\/jats:p>","DOI":"10.3390\/informatics10030063","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T01:15:45Z","timestamp":1690161345000},"page":"63","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6522-2128","authenticated-orcid":false,"given":"Dimitris C.","family":"Gkikas","sequence":"first","affiliation":[{"name":"Department of International & European Economic Studies, School of Economic Sciences, Athens University of Economics and Business, 76 Patision Str., 10434 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8133-4509","authenticated-orcid":false,"given":"Prokopis K.","family":"Theodoridis","sequence":"additional","affiliation":[{"name":"School of Social Sciences, Hellenic Open University, Patras Campus, 18 Aristotelous Str., 26335 Patras, Greece"}]},{"given":"Theodoros","family":"Theodoridis","sequence":"additional","affiliation":[{"name":"School of Science Engineering and Environment, University of Salford, The Crescent, Salford M5 4WT, UK"}]},{"given":"Marios C.","family":"Gkikas","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, School of Economics and Business, University of Patras, 1 M. Alexandrou Str., Koukouli, 26334 Patras, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,21]]},"reference":[{"key":"ref_1","unstructured":"Forina, M., Leardi, R., Armanino, C., and Lanteri, S. (1988). PARVUS: An Extendable Package of Programs for Data Exploration, Classification and Correlation, Version 3.0, Institute of Pharmaceutical and Food Analysis and Technologies."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"de Azambuja, R.X., Morais, A.J., and Filipe, V. (2023). X-Wines: A Wine Dataset for Recommender Systems and Machine Learning. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7010020"},{"key":"ref_3","first-page":"100261","article-title":"A machine learning application in wine quality prediction","volume":"8","author":"Bhardwaj","year":"2022","journal-title":"Mach. Learn. 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