Faculté des Sciences appliquées
Faculté des Sciences appliquées
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Game Intelligent Analyst - Anomaly Detection in Casino Games using Machine Learning Algorithms

Merchie, Florian ULiège
Promotor(s) : Wehenkel, Louis ULiège ; Boniver, Christophe
Date of defense : 25-Jun-2018/26-Jun-2018 • Permalink :
Title : Game Intelligent Analyst - Anomaly Detection in Casino Games using Machine Learning Algorithms
Translated title : [fr] Détection d'anomalies dans les jeux de casino par algorithmes d'apprentissage inductif
Author : Merchie, Florian ULiège
Date of defense  : 25-Jun-2018/26-Jun-2018
Advisor(s) : Wehenkel, Louis ULiège
Boniver, Christophe 
Committee's member(s) : Geurts, Pierre ULiège
Louppe, Gilles ULiège
Boniver, Aurélie 
Language : English
Number of pages : 75
Keywords : [en] machine learning
[en] casino
[en] extra trees
[en] supervised learning
[en] anomaly detection
[en] density ratio
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées


[en] Anomaly detection is a very large and complex field. In recent years, several
techniques based on data science were designed in order to improve the efficiency of
methods developed for this purpose. In particular, density-based anomaly detection
allows to estimate how much probability densities differ from each other by solving
a supervised learning problem using data drawn from these densities as a dataset.
Besides, the casino games company GAMING1 aims at automating the detection of
behavior modifications in its games when an update is performed. This master thesis introduces a method based on density-based anomaly detection to reach GAMING1’s objective. We explore two main approaches to solve this problem. The first
one relies on standard supervised learning algorithms. For this approach, two algorithms were considered: Extremely randomized trees and linear support vector machine. The second approach is based on deep learning and exploits recurrent neural
networks architectures. Using experimental cases of anomalies in casino games, it’s
been shown that extremely randomized trees outperforms both other methods as
far as performances and visual interpretation are concerned, but also considering
computational resources. In addition to accuracy, we rely on several other metrics
related to the comparison of probability densities in order to assess formal performances of the presented algorithm. We show that the tree-based method allows us
to distinguish irregular data with an accuracy of at least 0.7 for standard anomalies,
while providing relevant visual support thanks to probability densities representation. Moreover, it’s also possible to identify more hardly detectable anomalies by
using classifier calibration in order to enhance the visual support of the probability
densities, despite an associated low accuracy.



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  • Merchie, Florian ULiège Université de Liège > Master ingé. civ. info., à fin.


Committee's member(s)

  • Geurts, Pierre ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Algorith. des syst. en interaction avec le monde physique
    ORBi View his publications on ORBi
  • Louppe, Gilles ULiège Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data
    ORBi View his publications on ORBi
  • Boniver, Aurélie Société GAMING1
  • Total number of views 120
  • Total number of downloads 1172

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