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Ekip Oluşturma Sorunlarına Yönelik Makine Öğrenimi Uygulamalarına İlişkin Sistematik Bir Literatür Taraması

Yıl 2024, Cilt: 17 Sayı: 3, 175 - 188, 31.07.2024
https://doi.org/10.17671/gazibtd.1414527

Öz

Teknolojinin gelişmesiyle birlikte herhangi bir sürece ait tutulan veri çeşitliliği, veri sayısı katlanarak arttı. Bu verilerin işlenmesi ve analiz edilmesiyle bir çok problemin çözümü mümkün olabilmektedir. Ekip tarafından gerçekleştirilen faaliyetlerde en uygun ekip üyesinin seçimi ve doğru ekip oluşumu ekip çalışması başarısını ve sonucunu etkileyen unsurdur. Bu nedenle ekip üyesi seçimi, takım oluşturma problemi son yıllarda artan araştırma konularında biri olmuştur. Farklı disiplinlerden araştırmacılar, başarılı bir ekip oluşturma sürecini sağlayabilmek için araçlar, teknikler ve metodolojiler geliştirmeye çalışmaktadırlar. Makine Öğrenmesi (ML) yöntemleri takım oluşumu, ekip üyesi seçimi problemlerinde son yıllarda kullanılmaya başlayan yöntemlerden biri olmuştur. Bu problemin başarılı sonucu verilerin doğru bir şekilde toplanması, işlenmesi ve uygun makine öğrenme yöntemlerinin seçimine bağlıdır. Bu makalenin amacı takım oluşumu, ekip üyesi seçimi problemlerinde uygulanan makine öğrenme yöntemlerinin sistematik bir literatür taramasını sunmak, bu alanda hangi makine öğrenme metodlarının uygulandığını ve bunların performansını göstermektir. Altı bilimsel veri tabanında konuyla ilgili makaleler araştırılmıştır. Bu inceleme ML yöntemleri hakkında temel bilgiler sağlamanın yanısıra takım oluşumu problemlerinde yeni araştırma çalışmalarını da desteklemektedir.

Kaynakça

  • G. Stavrou, P. Adamidis, J. Papathanasiou, K. Tarabanis “Team Formation: A Systematic Literature Review”, Int. Journal of Business Science and Applied Management, 18(2), 2023.
  • J. Juárez, C. Santos, F. A. A. M. N. Soares, R. Vita, R. P. Francisco, J. P. Basto, S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance”, ACM Computing Surveys, 54(7), 2021. M. Ishi, J. Patil, J. Jhang, V. Patil, “An efficient team prediction for one day international matches using a hybrid approach of CS-PSO and machine learning algorithms”, Array 14, 2022.
  • T. P. Carvalho, C. Santos, C. A. Brizuela, “A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems”, Computers & Industrial Engineer, 2019.
  • D. Abidin, “A case study on player selection and team formation in football with machine learning”, Turkish Journal of Electrical Engineering & Computer Sciences, 29, 1672 – 1691, 2021.
  • W. Mengist, T. Soromessa, G. Legese, “Method for conducting systematic literature review and meta-analysis for environmental science research”, MethodsX 7, 2020.
  • K. Petersen, S. Vakkalanka, L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update” Information and Software Technology, 64, 1–18, 2015
  • I. El Naqa, M. J. Murphy, “What is machine learning?” In Machine Learning in Radiation Oncology, 3-11, 2015
  • M. Atalay, E. Çelik, “Artificial Intelligence and Machine Learning Applications in Big Data Analysis”, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172, 2017
  • E. Tosunoğlu, R. Yılmaz, E. Özeren, Z. Sağlam, “Eğitimde Makine Öğrenmesi: Araştırmalardaki Güncel Eğilimler Üzerine Inceleme”, Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 3(2), 178-199. 2021
  • M. Datta, B. Rudra, N. Mead, C. Rolland, “An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players”, 2nd International Conference on Intelligent Technologies (CONIT), 2022.
  • T. Shahriar, Y. Islam, N. Amin, “Player Classification Technique Based on Performance for a Soccer Team Using Machine Learning Algorithms”, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019.
  • M. Sumathi, S. Prabu, M. Rajkamal, “Cricket Players Performance Prediction and Evaluation Using Machine Learning Algorithms”, 2023 International Conference on Networking and Communications (ICNWC), 2023.
  • M. Shetty, S. Rane, C. Pandita, S. Salvi, “Machine learning-based Selection of Optimal sports Team based on the Players Performance”, Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020), 2020.
  • A. Santra, A. Sinha, P. Saha, A. K. Das, “A Novel Regression based Technique for Batsman Evaluation in the Indian Premier League”, 2020 IEEE International Conference for Convergence in Engineering, 2020.
  • N. Assavakamhaenghan, W. Tanaphantaruk, P. Suwanworaboon, M. Choetkiertikul, S. Tuarob, “Quantifying efectiveness of team recommendation for collaborative software development”, Automated Software Engineering, 2022.
  • C. Chang, M. Chang, J. Jhang, L. Yeh, C. Shen “Learning to Extract Expert Teams in Social Networks”, IEEE Transactions On Computational Social Systems, 9(5), 2022.
  • Z. Tanbour, D. Khudarieh, H. Abuodeh, A. Hawash, “Forming Software Development Team: Machine-Learning Approach”, 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2022.
  • S. Tuarob, N. Assavakamhaenghan, W. Tanaphantaruk, P. Suwanworaboon, S. Hassan, M. Choetkiertikul, “Automatic team recommendation for collaborative software development”, Empirical Software Engineering, 2021.
  • S. Ghar, S. Patil, W. Tanaphantaruk, V. Arunachalam, “Data Driven football scouting assistance with simulated player performance extrapolation”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021.
  • P. Keane, F. Ghaffar, D. Malone, “Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study”, Applied Network Science, 2020.
  • N. Assavakamhaenghan, P. Suwanworaboon, W. Tanaphantaruk, S. Tuarob, M. Choetkiertikul, “Towards Team Formation in Software Development: A Case Study of Moodle”, 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020.
  • H. Liu, M. Qiao, D. Greenia, R. Akkiraju, S. Dill, T. Nakamura, Y. Song, and H. M. Nezhad, “A machine learning approach to combining individual strength and team features for team recommendation,” Preceedings of The 13th International Conference on Machine Learning and Applications, 12, pp. 213–218, 2014.
  • R. Krishankumar, K. S. Ravichandran, “A Novel Trio Combo Strategy For Efficient Team Formation Using Hybrid Triangulation Mechanism”, ARPN Journal of Engineering and Applied Sciences, 11(5), 2016.
  • M. Tosato, J.Wu, “An Application Of Part To The Football Manager Data For Players Clusters Analyses To Inform Club Team Formation”, Big Data & Information Analytics. 3 (1), 43-54, 2018.
  • S. Buyrukoglu, S. Savas, “Stacked-Based Ensemble Machine Learning Model for Positioning Footballer”, Arabian Journal for Science and Engineering. 48, 1371-1383, 2022.
  • R. Maanijou, S. A. Mirroshandel, “Introducing an expert system for prediction of soccer player ranking using ensemble learning”, Neural Computing and Applications. 31, 9157-9174, 2019.
  • M. K. Manju, A. O. Philip, “Novel method for ranking batsmen in Indian Premier League”, Data Science and Management. 6, 158-173, 2023.
  • Y. Ke, R. Bian, R. Chandra, “A unified machine learning framework for basketball team roster construction: NBA and WNBA”, Applied Soft Computing. 153, 2024.
  • D. Tirtho, S. Rahman, S. Mahbub, “Cricketer’s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization”, Applied Soft Computing. 129, 2022.
  • M. Nouraie, C. Eslahchi, A. Baca, “Intelligent team formation and player selection: a data driven approach for football coaches”, Applied Intelligence. 53, 30250-30265, 2023.
  • G. Papageorgiou, V. Sarlis, C. Tjortjis, “An innovative method for accurate NBA player performance forecasting and line-up optimization in daily fantasy sports”, International Journal of Data Science and Analytics, 2024.
  • Z. Mahmood, A. Daud, R. A. Abbasi, “Using machine learning techniques for rising star prediction in basketball”, Knowledge-Based Systems, 211, 2021.
  • J. Brooks, M. Kerr, J. Guttag, “Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights”, KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 49-55, 2016.
  • T. L. Persson, H. Kozlica, N. Carlsson, P. Lambrix, “Prediction of Tiers in the Ranking of Ice Hockey Players”, 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, 89-100, 2020
  • L. Pappalardo, P. Cintia, P. Ferragina, E. Massucco, D. Pedreschi, F. Giannotti “PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach”, ACM Transactions on Intelligent Systems and Technology, 10(5), 1-27, 2019
  • A. Kaviya, A. S. Mishra, B. Valarmathi, Comprehensive Data Analysis and Prediction on IPL using Machine Learning Algorithms”, International Journal on Emerging Technologies, 2020.
  • T. P. Carvalho , A. A. Fabrízzio, M. N. Soaresa, , V. Roberto, P. F. Roberto, J. P. Bastoc , S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance”, Computers & Industrial Engineering. 137, 2019.
  • D. Xames, F. K. Torsha, F. Sarwar, “A systematic literature review on recent trends of machine learning applications in additive manufacturing”, Journal of Intelligent Manufacturing. 34, 2529–2555, 2023F

A Systematic Literature Review of Machine Learning Applications for Team Formation Problems

Yıl 2024, Cilt: 17 Sayı: 3, 175 - 188, 31.07.2024
https://doi.org/10.17671/gazibtd.1414527

Öz

With the development of technology, the variety and number of data held for any process has increased exponentially. By processing and analyzing this data, it is possible to solve many problems. Selection of the most appropriate team member and correct team formation in the activities carried out by the team are the factors that affect the success and result of teamwork. For this reason, the problem of team member selection and team formation has become one of the increasing research topics in recent years. Researchers from different disciplines are trying to develop tools, techniques and methodologies to ensure a successful team building process. Machine Learning (ML) methods have become one of the methods that have started to be used in team formation and team member selection problems in recent years. The successful outcome of this problem depends on the correct collection and processing of data and the selection of appropriate machine learning methods. The aim of this article is to present a systematic literature review of machine learning methods applied in team formation and team member selection problems, and to show which machine learning methods are applied in this field and their performance. Articles on the subject were searched in six scientific databases. In addition to providing fundamental information about ML methods, this review also supports new research efforts on team formation problems.

Kaynakça

  • G. Stavrou, P. Adamidis, J. Papathanasiou, K. Tarabanis “Team Formation: A Systematic Literature Review”, Int. Journal of Business Science and Applied Management, 18(2), 2023.
  • J. Juárez, C. Santos, F. A. A. M. N. Soares, R. Vita, R. P. Francisco, J. P. Basto, S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance”, ACM Computing Surveys, 54(7), 2021. M. Ishi, J. Patil, J. Jhang, V. Patil, “An efficient team prediction for one day international matches using a hybrid approach of CS-PSO and machine learning algorithms”, Array 14, 2022.
  • T. P. Carvalho, C. Santos, C. A. Brizuela, “A Comprehensive Review and a Taxonomy Proposal of Team Formation Problems”, Computers & Industrial Engineer, 2019.
  • D. Abidin, “A case study on player selection and team formation in football with machine learning”, Turkish Journal of Electrical Engineering & Computer Sciences, 29, 1672 – 1691, 2021.
  • W. Mengist, T. Soromessa, G. Legese, “Method for conducting systematic literature review and meta-analysis for environmental science research”, MethodsX 7, 2020.
  • K. Petersen, S. Vakkalanka, L. Kuzniarz, “Guidelines for conducting systematic mapping studies in software engineering: An update” Information and Software Technology, 64, 1–18, 2015
  • I. El Naqa, M. J. Murphy, “What is machine learning?” In Machine Learning in Radiation Oncology, 3-11, 2015
  • M. Atalay, E. Çelik, “Artificial Intelligence and Machine Learning Applications in Big Data Analysis”, Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155-172, 2017
  • E. Tosunoğlu, R. Yılmaz, E. Özeren, Z. Sağlam, “Eğitimde Makine Öğrenmesi: Araştırmalardaki Güncel Eğilimler Üzerine Inceleme”, Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 3(2), 178-199. 2021
  • M. Datta, B. Rudra, N. Mead, C. Rolland, “An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players”, 2nd International Conference on Intelligent Technologies (CONIT), 2022.
  • T. Shahriar, Y. Islam, N. Amin, “Player Classification Technique Based on Performance for a Soccer Team Using Machine Learning Algorithms”, 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019.
  • M. Sumathi, S. Prabu, M. Rajkamal, “Cricket Players Performance Prediction and Evaluation Using Machine Learning Algorithms”, 2023 International Conference on Networking and Communications (ICNWC), 2023.
  • M. Shetty, S. Rane, C. Pandita, S. Salvi, “Machine learning-based Selection of Optimal sports Team based on the Players Performance”, Proceedings of the Fifth International Conference on Communication and Electronics Systems (ICCES 2020), 2020.
  • A. Santra, A. Sinha, P. Saha, A. K. Das, “A Novel Regression based Technique for Batsman Evaluation in the Indian Premier League”, 2020 IEEE International Conference for Convergence in Engineering, 2020.
  • N. Assavakamhaenghan, W. Tanaphantaruk, P. Suwanworaboon, M. Choetkiertikul, S. Tuarob, “Quantifying efectiveness of team recommendation for collaborative software development”, Automated Software Engineering, 2022.
  • C. Chang, M. Chang, J. Jhang, L. Yeh, C. Shen “Learning to Extract Expert Teams in Social Networks”, IEEE Transactions On Computational Social Systems, 9(5), 2022.
  • Z. Tanbour, D. Khudarieh, H. Abuodeh, A. Hawash, “Forming Software Development Team: Machine-Learning Approach”, 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2022.
  • S. Tuarob, N. Assavakamhaenghan, W. Tanaphantaruk, P. Suwanworaboon, S. Hassan, M. Choetkiertikul, “Automatic team recommendation for collaborative software development”, Empirical Software Engineering, 2021.
  • S. Ghar, S. Patil, W. Tanaphantaruk, V. Arunachalam, “Data Driven football scouting assistance with simulated player performance extrapolation”, 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021.
  • P. Keane, F. Ghaffar, D. Malone, “Using machine learning to predict links and improve Steiner tree solutions to team formation problems - a cross company study”, Applied Network Science, 2020.
  • N. Assavakamhaenghan, P. Suwanworaboon, W. Tanaphantaruk, S. Tuarob, M. Choetkiertikul, “Towards Team Formation in Software Development: A Case Study of Moodle”, 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2020.
  • H. Liu, M. Qiao, D. Greenia, R. Akkiraju, S. Dill, T. Nakamura, Y. Song, and H. M. Nezhad, “A machine learning approach to combining individual strength and team features for team recommendation,” Preceedings of The 13th International Conference on Machine Learning and Applications, 12, pp. 213–218, 2014.
  • R. Krishankumar, K. S. Ravichandran, “A Novel Trio Combo Strategy For Efficient Team Formation Using Hybrid Triangulation Mechanism”, ARPN Journal of Engineering and Applied Sciences, 11(5), 2016.
  • M. Tosato, J.Wu, “An Application Of Part To The Football Manager Data For Players Clusters Analyses To Inform Club Team Formation”, Big Data & Information Analytics. 3 (1), 43-54, 2018.
  • S. Buyrukoglu, S. Savas, “Stacked-Based Ensemble Machine Learning Model for Positioning Footballer”, Arabian Journal for Science and Engineering. 48, 1371-1383, 2022.
  • R. Maanijou, S. A. Mirroshandel, “Introducing an expert system for prediction of soccer player ranking using ensemble learning”, Neural Computing and Applications. 31, 9157-9174, 2019.
  • M. K. Manju, A. O. Philip, “Novel method for ranking batsmen in Indian Premier League”, Data Science and Management. 6, 158-173, 2023.
  • Y. Ke, R. Bian, R. Chandra, “A unified machine learning framework for basketball team roster construction: NBA and WNBA”, Applied Soft Computing. 153, 2024.
  • D. Tirtho, S. Rahman, S. Mahbub, “Cricketer’s tournament-wise performance prediction and squad selection using machine learning and multi-objective optimization”, Applied Soft Computing. 129, 2022.
  • M. Nouraie, C. Eslahchi, A. Baca, “Intelligent team formation and player selection: a data driven approach for football coaches”, Applied Intelligence. 53, 30250-30265, 2023.
  • G. Papageorgiou, V. Sarlis, C. Tjortjis, “An innovative method for accurate NBA player performance forecasting and line-up optimization in daily fantasy sports”, International Journal of Data Science and Analytics, 2024.
  • Z. Mahmood, A. Daud, R. A. Abbasi, “Using machine learning techniques for rising star prediction in basketball”, Knowledge-Based Systems, 211, 2021.
  • J. Brooks, M. Kerr, J. Guttag, “Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights”, KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 49-55, 2016.
  • T. L. Persson, H. Kozlica, N. Carlsson, P. Lambrix, “Prediction of Tiers in the Ranking of Ice Hockey Players”, 7th International Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2020, 89-100, 2020
  • L. Pappalardo, P. Cintia, P. Ferragina, E. Massucco, D. Pedreschi, F. Giannotti “PlayeRank: Data-driven Performance Evaluation and Player Ranking in Soccer via a Machine Learning Approach”, ACM Transactions on Intelligent Systems and Technology, 10(5), 1-27, 2019
  • A. Kaviya, A. S. Mishra, B. Valarmathi, Comprehensive Data Analysis and Prediction on IPL using Machine Learning Algorithms”, International Journal on Emerging Technologies, 2020.
  • T. P. Carvalho , A. A. Fabrízzio, M. N. Soaresa, , V. Roberto, P. F. Roberto, J. P. Bastoc , S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance”, Computers & Industrial Engineering. 137, 2019.
  • D. Xames, F. K. Torsha, F. Sarwar, “A systematic literature review on recent trends of machine learning applications in additive manufacturing”, Journal of Intelligent Manufacturing. 34, 2529–2555, 2023F
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Karar Desteği ve Grup Destek Sistemleri, Makine Öğrenme (Diğer)
Bölüm Derleme
Yazarlar

Soner Karataş 0000-0002-9084-9758

Hüseyin Çakır 0000-0001-9424-2323

Yayımlanma Tarihi 31 Temmuz 2024
Gönderilme Tarihi 4 Ocak 2024
Kabul Tarihi 16 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 17 Sayı: 3

Kaynak Göster

APA Karataş, S., & Çakır, H. (2024). A Systematic Literature Review of Machine Learning Applications for Team Formation Problems. Bilişim Teknolojileri Dergisi, 17(3), 175-188. https://doi.org/10.17671/gazibtd.1414527