Makine öğrenimi ve ÇKKV tekniklerinin birleştirilmesi: genişletilmiş bir bibliyometrik analiz

Year 2024, , 642 - 657, 31.07.2024

Mehmet Asaf Düzen , İsmail Buğra Bölükbaşı , Eyüp Çalık

https://doi.org/10.61112/jiens.1475948

Abstract

Makine Öğrenmesi (ML) ve Çok Kriterli Karar Verme (MCDM) son zamanlarda birçok farklı alanda yaygın olarak kullanılan popüler yöntemlerdir. Bu iki yöntemin birlikte kullanımının artması nedeniyle bu alanda bibliyometrik bir analize ihtiyaç duyulmaktadır. Bu çalışmada, Ocak 2000 ile Nisan 2024 tarihleri arasında Web of Science (WoS) ve Scopus veri tabanlarından alınan 1189 makale üzerinde yazar tarafından geliştirilen genişletilmiş bir bibliyometrik analiz gerçekleştirilmiştir. İlk bibliyometrik analizde, genel bir bölüm olarak, verileri anlamlı hale getirmek için VOSviewer programı kullanılmıştır. Özellikle anahtar kelime analizi ile ilgili yıllara ve ilişkilere göre analiz gerçekleştirilmiştir. Ayrıca, en sık kullanılan anahtar kelimeler belirlenmiş ve eğilimin yönü tespit edilmiştir. İlk bibliyometrik analiz sırasında, WoS veri tabanından 297 yayın ve Scopus'tan 11 yayın olmak üzere 308 makale analiz edilmiştir. Çalışma, bibliyometrik analizin genişletilmiş bir parçası olarak yeni modeller ve kategoriler oluşturarak mevcut literatürden ayrılıyor. Bu model ve kategorileri kullanarak, araştırmacıların makine öğrenimi ve ÇKKV'yi birlikte nasıl kullandıkları ve bu yöntemlerin ne yönde geliştiği sorularına yanıt aradık. Bu bağlamda, model ve kategorilerin farklı araştırma alanlarındaki dağılımı ve yıllar içindeki değişimleri analiz edildi. Bu çalışma, araştırmacılara makine öğrenimi ve ÇKKV tekniklerini entegre ederken çeşitli kombinasyon olasılıkları hakkında kapsamlı bir bakış açısı sunmaktadır.

Keywords

Makine öğrenmesi, Çok kriterli karar verme, Bibliyometrik analiz, VOSviewer

How to combine ML and MCDM techniques: an extended bibliometric analysis

Abstract

Machine Learning (ML) and Multi Criteria Decision Making (MCDM) are popular methods that have recently been widely used in many different fields. Due to the increasing use of these two methods together, there is a need for a bibliometric analysis in this area. In this study, an extended author-developed bibliometric analysis was performed on 1189 publications retrieved from the Web of Science (WoS) and Scopus databases between January 2000 and April 2024. In the initial bibliometric analysis, as a generic part, the VOSviewer program was used to make the data meaningful. In particular, the analysis was carried out according to years and relationships related to the keyword analysis. In addition, the most frequently used keywords were identified, and the direction of the trend was determined. During the initial bibliometric analysis, 308 publications were analyzed, with 297 publications retrieved from the WoS database and 11 publications from Scopus. The study distinguishes itself from the existing literature by establishing new models and categories as an extended part of bibliometric analysis. Using these models and categories, we sought to answer questions about how researchers use ML and MCDM together and in what direction these methods are evolving. In this context, the distribution of models and categories in different research areas and their changes over the years were analyzed. This study provides researchers with a comprehensive perspective on the various combination possibilities when integrating ML and MCDM techniques.

Keywords

Machine learning, Multi criteria decision making, Bibliometric analysis, VOSviewer

References

• Alpaydin E (2014) Introduction to machine learning. The MIT Press, Cambridge
• Sandeep MS, Tiprak K, Kaewunruen S, Pheinsusom P, Pansuk W (2023) Shear strength prediction of reinforced concrete beams using machine learning. Structures 47:1196–1211. https://doi.org/10.1016/j.istruc.2022.11.140
• Kou G, Wu W (2014) An analytic hierarchy model for classification algorithms selection in credit risk analysis. Math Prob in Eng. https://doi.org/10.1155/2014/297563
• Ali R, Lee S, Chung TC (2017) Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Exp Syst with Appl 71,257–278. https://doi.org/10.1016/j.eswa.2016.11.034
• Golmohammadi D, Zhao L, Dreyfus D (2023) Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics. Omega 120:102907. https://doi.org/10.1016/j.omega.2023.102907
• Taherdoost H, Madanchian M (2023) Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia 3(1):77–87. https://doi.org/10.3390/encyclopedia3010006
• Chowdhury NK, Kabir MA, Rahman MM, Islam SMS (2022). Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comp in Bio and Med 145:105405. https://doi.org/10.1016/j.compbiomed.2022.105405
• Yilmaz,I, Adem A, Dağdeviren M (2023) A machine learning-integrated multi-criteria decision-making approach based on consensus for selection of energy storage locations. J of Energy Storage 69:107941. https://doi.org/10.1016/j.est.2023.107941
• Choudhary S, Pingale SM, Khare D (2022) Delineation of groundwater potential zones of upper Godavari sub-basin of India using bi-variate, MCDM and advanced machine learning algorithms. Geo Inter 37(27): 15063–15093. https://doi.org/10.1080/10106049.2022.2093992
• Mustapha MT, Ozsahin DU, Ozsahin I, Uzun B (2022) Breast cancer screening based on supervised learning and multi-criteria decision-making. Diagnostics 12(6), 1326. https://doi.org/10.3390/diagnostics12061326
• Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using dempster shafer theory. J of Hydro, 590, 125275. Elsevier B.V. https://doi.org/10.1016/j.jhydrol.2020.125275
• Viviani M, Pasi G (2017) A multi-criteria decision making approach for the assessment of information credibility in social media. 11th International Workshop, Naples, Italy, Dec. 19-21.
• Çalık E (2021) İşgören seçiminde makine öğrenmesi ve çok kriterli karar verme yöntemlerinin birlikte kullanımının değerlendirilmesi. J of Turk Stud 16:1483–1494. https://doi.org/10.7827/turkishstudies.51593
• Liao H, He Y, Wu X, Wu Z, Bausys R (2023) Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review. Information Fusion. https://doi.org/10.1016/j.inffus.2023.101970
• Andres C, Verastegui A, Elizabet N, Gilvonio C, Esthefani M, Flores R (2023) A bibliometrics study of plants, animals, bacteria, algae and technologies that reduce, filter and eliminate microplastics from planet earth, ecological solutions for the environment. Deci Sci Letters 12:773–782. https://doi.org/10.5267/dsl.2023.6.004
• Fernández JMM, Moreno JJG, González EPV, Iglesias GA (2022) Bibliometric analysis of the application of artificial intelligence techniques to the management of innovation projects. Appl Sci 12(22). https://doi.org/10.3390/app122211743
• Pritchard A (1969) Statistical bibliography or bibliometrics? J of Doc. 25(4):348.
• Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM (2021) How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research 133:285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
• Gao C, Sun M, Geng Y, Wu R, Chen W (2016) A bibliometric analysis based review on wind power price. Applied Energy 182:602–612. https://doi.org/10.1016/j.apenergy.2016.08.144
• Ellegaard O, Wallin JA (2015) The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 105(3):1809–1831. https://doi.org/10.1007/s11192-015-1645-z
• Demir G, Chatterjee P, Pamucar D (2024) Sensitivity analysis in multi-criteria decision making: A state-of-the-art research perspective using bibliometric analysis. Exp Syst with Appl 237, 121660. https://doi.org/10.1016/j.eswa.2023.121660
• Riahi Y, Saikouk T, Gunasekaran A, Badraoui I (2021) Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Exp Syst with Appl 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702
• Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372. https://doi.org/10.1136/bmj.n71
• Xu Z, Ge Z, Wang X, Skare M (2021) Bibliometric analysis of technology adoption literature published from 1997 to 2020. Tech For and Soc Cha 170. https://doi.org/10.1016/j.techfore.2021.120896
• Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Prakash I (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J of Hydro 573:311-323. https://doi.org/10.1016/j.jhydrol.2019.03.073
• Aertsen W, Kint V, Orshoven JV, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in mediterranean mountain forests. Eco Model 221(8):1119-1130. https://doi.org/10.1016/j.ecolmodel.2010.01.007
• Ma X, Liu C, Wen H, Wang Y, Wu YJ (2017) Understanding commuting patterns using transit smart card data. J of Trans Geo 58:135-145. https://doi.org/10.7307/ptt.v32i1.3052
• Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using dempster shafer theory. J of Hydro 590:125275. https://doi.org/10.1016/j.jhydrol.2020.125275
• Ali SA, Parvin F, Pham QB, Vojtek M, Vojteková J, Costache R, Ghorbani MA (2020) GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: a case of Topľa basin, Slovakia. Eco Ind 117(106620). https://doi.org/10.1016/j.ecolind.2020.106620
• Costache R, Pham QB, Sharifi E, Linh NTT, Abba SI, Vojtek M, Khoi, DN (2019) Flash-flood susceptibility assessment using multi-criteria decision making and machine learning supported by remote sensing and GIS techniques. Remote Sensing 12(1):106. https://doi.org/10.3390/rs12010106
• Arabameri A, Rezaei K, Cerda A, Lombardo L, Comino JR(2019) GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci of the Total Env 658:160-177. https://doi.org/10.1016/j.scitotenv.2018.12.115
• Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst and Env 3:585-601. https://doi.org/10.1007/s41748-019-00123-y
• Peng Y, Zhang Y, Tang Y, Li S (2011) An incident information management framework based on data integration, data mining, and multi-criteria decision making. Deci Supp Syst 51(2):316-327. https://doi.org/10.1016/j.dss.2010.11.025
• Ahani A, Nilashi M, Yadegaridehkordi E, Sanzogni L, Tarik AR, Knox K, Ibrahim O (2019) Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. J of Ret and Cons Serv 51:331-343. https://doi.org/10.1016/j.jretconser.2019.06.014