AI 2004: Advances in Artificial Intelligence

Webb, Geoffrey I.; Yu, Xinghuo

doi:10.1007/b104336

Cited by 4 publications

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Propositionalisation of Multi-instance Data Using Random Forests

Frank

Pfahringer

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Multi-instance learning is a generalisation of attribute-value learning where examples for learning consist of labeled bags (i.e. multisets) of instances. This learning setting is more computationally challenging than attribute-value learning and a natural fit for important application areas of machine learning such as classification of molecules and image classification. One approach to solve multi-instance learning problems is to apply propositionalisation, where bags of data are converted into vectors of attribute-value pairs so that a standard propositional (i.e. attribute-value) learning algorithm can be applied. This approach is attractive because of the large number of propositional learning algorithms that have been developed and can thus be applied to the propositionalised data. In this paper, we empirically investigate a variant of an existing propositionalisation method called TLC. TLC uses a single decision tree to obtain propositionalised data. Our variant applies a random forest instead and is motivated by the potential increase in robustness that this may yield. We present results on synthetic and real-world data from the above two application domains showing that it indeed yields increased classification accuracy when applying boosting and support vector machines to classify the propositionalised data.

show abstract

Propositionalisation of Multi-instance Data Using Random Forests

Frank

Pfahringer

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals

et al. 2016

View full text Add to dashboard Cite

Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning

Mohandas,

Ahsan,

Haider

2023

BDCC

View full text Add to dashboard Cite

Football (also known as Soccer), boasts a staggering fan base of 3.5 billion individuals spread across 200 countries, making it the world’s most beloved sport. The widespread adoption of advanced technology in sports has become increasingly prominent, empowering players, coaches, and team management to enhance their performance and refine team strategies. Among these advancements, player substitution plays a crucial role in altering the dynamics of a match. However, due to the absence of proven methods or software capable of accurately predicting substitutions, these decisions are often based on instinct rather than concrete data. The purpose of this research is to explore the potential of employing machine learning algorithms to predict substitutions in Football, and how it could influence the outcome of a match. This study investigates the effect of timely and tactical substitutions in football matches and their influence on the match results. Machine learning techniques such as Logistic Regression (LR), Decision tree (DT), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Random Forest (RF) classifiers were implemented and tested to develop models and to predict player substitutions. Relevant data was collected from the Kaggle dataset, which contains data of 51,738 substitutions from 9074 European league football matches in 5 leagues spanning 6 seasons. Machine learning models were trained and tested using an 80-20 data split and it was observed that RF model provided the best accuracy of over 70% and the best F1-score of 0.65 on the test set across all football leagues. SVM model achieved the best Precision of almost 0.8. However, the worst computation time of up to 2 min was consumed. LR showed some overfitting issues with 100% accuracy in the training set, but only 60% accuracy was obtained for the test set. To conclude, based on the time of substitution and match score-line, it was possible to predict the players who can be substituted, which can provide a match advantage. The achieved results provided an effective way to decide on player substitutions for both the team manager and coaches.

show abstract

AI 2004: Advances in Artificial Intelligence

Cited by 4 publications

References 7 publications

Propositionalisation of Multi-instance Data Using Random Forests

Propositionalisation of Multi-instance Data Using Random Forests

Use of a Machine-learning Method for Predicting Highly Cited Articles Within General Radiology Journals

Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning

Contact Info

Product

Resources

About