Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System

Al-Asadi, Mustafa A.; Taşdemir, Şakir

doi:10.1109/access.2021.3124931

Cited by 32 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This game includes over 19,000 players, more than 700 teams, and 30 global events 70 , providing a rich dataset invaluable for coaches and sports analysis 71 , 72 . The use of this video game data for analytical purposes, including predicting match outcomes and evaluating player wages with machine learning, has become increasingly popular since 2014 73 , 74 . For example, researchers have utilized it in machine learning projects to accurately predict match results 75 and determine if a player’s wage is above or below the median based on age and overall attributes 76 .…”

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 on football attacking players’ match technical performance: a longitudinal study

Luo,

Sun,

Guo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This study examined the impact of COVID-19 on 28 indicators of match technical performance (MTP) for football attacking players upon their return to play. Analyzing data from 100 players in the Big Five European football leagues, covering 1500 matches each before and after COVID-19 over 3 years (2020–2023), revealed significant differences in 76% of players’ MTP indicators. Notably, 14 indicators, particularly the five indicators linked to scoring, significantly decreased post-COVID-19. On average, players needed 3.09 matches to regain pre-infection MTP levels. The impact varied across player groups, with those in the elite group showing a milder effect; they required an average of 2.64 matches for recovery, compared to the control group’s 3.55 matches. We found that, with increasing age, the majority of players’ MTP indicators did not exhibit significant changes, both before and after they contracted COVID-19. In conclusion, the study highlighted the negative impact of COVID-19 on football attacking players’ MTP. Players in the elite group experienced fewer adverse effects than those in the control group. This insight assisted coaches and managers in evaluating the impact of COVID-19 and similar virus-induced illnesses on players’ MTP, enabling them to formulate training regimens for recovery and specific match tactics upon players' return to play.

show abstract

Section: Methodsmentioning

confidence: 99%

Impact of COVID-19 on football attacking players’ match technical performance: a longitudinal study

Luo,

Sun,

Guo

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In a recent research, the researchers have used random oversampling and synthetic minority oversampling technique to address the class imbalance and high dimensionality problem of characterisation of football players [47]. Comparisons are carried out over nine different feature selection algorithms and three balancing techniques.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Jatain,

Niranjanamurthy,

Dayananda

2024

SN COMPUT. SCI.

View full text Add to dashboard Cite

With more and more teaching learning activities being shifted to online mode, the education system has seen a drastic paradigm shift in the recent times. Learner opinion has emerged as an important metric for gaining valuable insights about teaching–learning process, student satisfaction, course popularity, etc. Traditional methods for opinion mining of learner feedback are tedious and require manual intervention. The author, in this work has proposed a hybrid bio-inspired metaheuristic feature selection approach for opinion mining of learner comments regarding a course. Experimental work is conducted over a real-world education dataset comprising of 110 K learner comments (referred to as Educational Dataset now onwards) collected from Coursera and learner data from academic institution MSIT. Based on the experimental results over the collected dataset, the proposed model achieves an accuracy of 92.24%. Further, for comparative analysis, results of the proposed model are compared with the ENN models for different embeddings, viz., Word2Vec, tf-idf and domain-specific embedding for the SemEval-14 Task 4. The hybrid bio-inspired metaheuristic model outperforms the pre-existing models for the standard dataset too.

show abstract

“…The metrics were calculated across different classes: True Positives (TPs) refer to the number of correctly classified positive instances, while True Negatives (TNs) represent the number of correctly classified negative instances. False Positives (FPs) indicate the number of instances that are falsely classified as positive, and False Negatives (FNs) denote the instances that are falsely classified as negative [18,39]. Let N represent the total number of samples, and the evaluation metrics can be expressed using the following formulas:…”

Section: Performance Measuresmentioning

confidence: 99%

Novel Machine Learning Based Credit Card Fraud Detection Systems

Feng,

Kim

2024

Mathematics

View full text Add to dashboard Cite

This research deals with the critical issue of credit card fraud, a problem that has escalated in the last decade due to the significant increase in credit card usage, largely driven by advances in international trade, e-commerce, and FinTech. With global losses projected to exceed USD 400 billion in the next decade, the urgent need for effective fraud detection systems is apparent. Our study leverages the power of machine learning (ML) and presents a novel approach to credit card fraud detection. We used the European cardholders dataset for model training, addressing the data imbalance issue that often hinders the effectiveness of the learning process. As a key innovative element, we introduce compact data learning (CDL), a powerful tool for reducing the size and complexity of the training dataset without sacrificing the accuracy of the ML system. Comparative experiments have shown that our CDL-adapted feature reduction outperforms various ML algorithms and feature reduction methods. The findings of this research not only contribute to the theoretical foundations of fraud detection but also provide practical implications for the financial sector, which can benefit immensely from the enhanced fraud detection system.

show abstract

Empirical Comparisons for Combining Balancing and Feature Selection Strategies for Characterizing Football Players Using FIFA Video Game System

Cited by 32 publications

References 42 publications

Impact of COVID-19 on football attacking players’ match technical performance: a longitudinal study

Impact of COVID-19 on football attacking players’ match technical performance: a longitudinal study

A Hybrid Bio-inspired Fuzzy Feature Selection Approach for Opinion Mining of Learner Comments

Novel Machine Learning Based Credit Card Fraud Detection Systems

Contact Info

Product

Resources

About