Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System
Yazarlar (2)
Prof. Dr. Şakir TAŞDEMİR Selçuk Üniversitesi, Türkiye
Mustafa A. Al-Asadı
Selçuk Üniversitesi, Türkiye
Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | IEEE Access (Q2) | ||
| Dergi ISSN | 2169-3536 Dergi Bilgileri (2021) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2021 |
| Cilt / Sayı / Sayfa | 9 / 1 / 149266–149286 | DOI | 10.1109/ACCESS.2021.3124931 |
| Makale Linki | https://ieeexplore.ieee.org/abstract/document/9598823 | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| The process of modelling individual player performance using machine learning is a mature task in sports analytics. The most significant challenges in machine learning include class imbalance and high dimensionality problems. We conducted a comprehensive literature review and observed that both the issues have been studied independently. We found that feature selection addresses the dimensionality reduction problem by determining a subset of relevant features, while data sampling seeks to make the data more balanced by adding or removing instances. We also found out that efforts have been taken for studying the effect of the joint use of feature selection and balancing techniques. However, the prioritization of the feature selection and sampling is still difficult, and the relationship between them remains unclear. This paper presents a large-scale comparison of characterizing football players into nine … |
| Anahtar Kelimeler |
| Class imbalance | Data mining | Data sampling | Feature selection | FIFA video game | Player characterizing |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 17 |
| Scopus | 34 |
| Google Scholar | 45 |