{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:02:58Z","timestamp":1780383778294,"version":"3.54.1"},"reference-count":24,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T00:00:00Z","timestamp":1653955200000},"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>An excessive amount of data is generated daily. A consumer\u2019s journey has become extremely complicated due to the number of electronic platforms, the number of devices, the information provided, and the number of providers. The need for artificial intelligence (AI) models that combine marketing data and computer science methods is imperative to classify users\u2019 needs. This work bridges the gap between computer and marketing science by introducing the current trends of AI models on marketing data. It examines consumers\u2019 behaviour by using a decision-making model, which analyses the consumer\u2019s choices and helps the decision-makers to understand their potential clients\u2019 needs. This model is able to predict consumer behaviour both in the digital and physical shopping environments. It combines decision trees (DTs) and genetic algorithms (GAs) through one wrapping technique, known as the GA wrapper method. Consumer data from surveys are collected and categorised based on the research objectives. The GA wrapper was found to perform exceptionally well, reaching classification accuracies above 90%. With regard to the Gender, the Household Size, and Household Monthly Income classes, it manages to indicate the best subsets of specific genes that affect decision making. These classes were found to be associated with a specific set of variables, providing a clear roadmap for marketing decision-making.<\/jats:p>","DOI":"10.3390\/informatics9020045","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T09:24:29Z","timestamp":1653989069000},"page":"45","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6522-2128","authenticated-orcid":false,"given":"Dimitris C.","family":"Gkikas","sequence":"first","affiliation":[{"name":"Department of Business Administration of Food and Agricultural Enterprises, School of Economics and Business, University of Patras, 2 George Seferi Str., 30100 Agrinio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8133-4509","authenticated-orcid":false,"given":"Prokopis K.","family":"Theodoridis","sequence":"additional","affiliation":[{"name":"Department of Business Administration of Food and Agricultural Enterprises, School of Economics and Business, University of Patras, 2 George Seferi Str., 30100 Agrinio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6896-5218","authenticated-orcid":false,"given":"Grigorios N.","family":"Beligiannis","sequence":"additional","affiliation":[{"name":"Department of Business Administration of Food and Agricultural Enterprises, School of Economics and Business, University of Patras, 2 George Seferi Str., 30100 Agrinio, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MSP.2004.1296540","article-title":"A Generic Applied Evolutionary Hybrid Technique","volume":"21","author":"Beligiannis","year":"2004","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1007\/11608288_87","article-title":"GA SVM Wrapper Ensemble for Keystroke Dynamics Authentication","volume":"3832","author":"Sung","year":"2005","journal-title":"Adv. Biom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.cie.2006.07.004","article-title":"Ensemble Based on GA Wrapper Feature Selection","volume":"51","author":"Yu","year":"2006","journal-title":"Comput. Ind. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1825","DOI":"10.1016\/j.patrec.2007.05.011","article-title":"A Hybrid Genetic Algorithm for Feature Selection Wrapper Based on Mutual Information","volume":"28","author":"Huang","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1676","DOI":"10.1016\/j.patcog.2007.10.013","article-title":"Genetic Algorithm-based Feature Set Partitioning for Classification Problems","volume":"41","author":"Rokach","year":"2008","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.cam.2017.04.036","article-title":"A Hybrid System with Filter Approach and Multiple Population Genetic Algorithm for Feature Selection in Credit scoring","volume":"329","author":"Wang","year":"2018","journal-title":"J. Comput. Appl. Math."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Soufan, O., Kleftogiannis, D., Kalnis, P., and Bajic, V. (2015). DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0117988"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"012087","DOI":"10.1088\/1742-6596\/978\/1\/012087","article-title":"Comparison of Naive Bayes and Decision Tree on Feature Selection Using Genetic Algorithm for Classification Problem","volume":"978","author":"Rahmadani","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s12293-018-0269-2","article-title":"A Multi-objective Hybrid Filter-Wrapper Evolutionary Approach for Feature Selection","volume":"11","author":"Hammami","year":"2018","journal-title":"Memetic Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dogadina, E.P., Smirnov, M.V., Osipov, A.V., and Suvorov, S.V. (2021). Evaluation of the Forms of Education of High School Students Using a Hybrid Model Based on Various Optimization Methods and a Neural Network. Informatics, 8.","DOI":"10.3390\/informatics8030046"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chowdhury, A., Rosenthal, J., Waring, J., and Umeton, R. (2021). Applying Self-Supervised Learning to Medicine: Review of the State of the Art and Medical Implementations. Informatics, 8.","DOI":"10.20944\/preprints202108.0238.v1"},{"key":"ref_12","unstructured":"Kohavi, R. (1996). Wrappers for Performance Enhancement and Oblivious Decision Graphs. [Ph.D. Thesis, Stanford University]."},{"key":"ref_13","unstructured":"Mitchell, T.M. (1997). Machine Learning, McGraw-Hill."},{"key":"ref_14","unstructured":"Russel, S., and Norvig, P. (2003). Artificial Intelligence: A Modern Approach, Prentice Hall. [3rd ed.]."},{"key":"ref_15","unstructured":"Witten, I., Frank, E., and Hall, M. (2011). Data Mining, Morgan Kaufmann Publishers."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of Decision Trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/S0020-7373(87)80053-6","article-title":"Simplifying Decision Trees","volume":"27","author":"Quinlan","year":"1987","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0004-3702(98)00034-4","article-title":"Top-down Induction of First-order Logical Decision Trees","volume":"101","author":"Blockeel","year":"1998","journal-title":"Artif. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mitchell, M. (1996). An Introduction to Genetic Algorithms, The MIT Press.","DOI":"10.7551\/mitpress\/3927.001.0001"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","article-title":"Genetic Algorithm Tutorial","volume":"4","author":"Whitley","year":"1994","journal-title":"Stat. Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ins.2003.03.019","article-title":"Genetic Wrappers for Feature Selection in Decision Tree Induction and Variable Ordering in Bayesian Network Structure learning","volume":"163","author":"Hsu","year":"2004","journal-title":"Inf. Sci."},{"key":"ref_22","unstructured":"Davis, L. (1991). Handbook of Genetic Algorithms, Van Nostrand Reinhold."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kavoura, A., Havlovic, S.J., and Totskaya, N. (2021). The Impact of COVID-19 on Consumer Behaviour: The Case of Greece. Strategic Innovative Marketing and Tourism in the COVID-19 Era, Springer Proceedings in Business and Economics, Springer.","DOI":"10.1007\/978-3-030-66154-0"},{"key":"ref_24","unstructured":"(2020, August 15). Data Ethics. Available online: https:\/\/dataethics.eu\/danish-companies-behind-seal-for-digital-responsibility."}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/2\/45\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:22:33Z","timestamp":1760138553000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/9\/2\/45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":24,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["informatics9020045"],"URL":"https:\/\/doi.org\/10.3390\/informatics9020045","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]}}}