A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer)

Due to the low scoring nature of football (soccer), shots are often used as a proxy to evaluate team and player performances. However, not all shots are created equally and their quality differs significantly depending on the situation. The aim of this study is to objectively quantify the quality of any given shot by introducing a so-called expected goals (xG) model. This model is validated statistically and with professional match analysts. The best performing model uses an extreme gradient boosting algorithm and is based on hand-crafted features from synchronized positional and event data of 105, 627 shots in the German Bundesliga. With a ranked probability score (RPS) of 0.197, it is more accurate than any previously published expected goals model. This approach allows us to assess team and player performances far more accurately than is possible with traditional metrics by focusing on process rather than results.

An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir

Information Sciences ◽

10.1016/j.ins.2021.04.063 ◽

2021 ◽

Author(s):

Hai Tao ◽

Maria Habib ◽

Ibrahim Aljarah ◽

Hossam Faris ◽

Haitham Abdulmohsin Afan ◽

...

Keyword(s):

Gradient Boosting ◽

Algorithm Development ◽

Prediction of quality of life in people with ALS

ACM SIGAPP Applied Computing Review ◽

10.1145/3477127.3477128 ◽

2021 ◽

Vol 21 (2) ◽

pp. 5-17

Author(s):

Anna Markella Antoniadi ◽

Miriam Galvin ◽

Mark Heverin ◽

Orla Hardiman ◽

Catherine Mooney

Keyword(s):

Quality Of Life ◽

Disease Onset ◽

Model Performance ◽

Sampling Technique ◽

Clinical Decision ◽

Gradient Boosting ◽

Rapid Decline ◽

Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).

Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware

Sensors ◽

10.3390/s19061449 ◽

2019 ◽

Vol 19 (6) ◽

pp. 1449 ◽

Cited By ~ 2

Author(s):

Yulong Shi ◽

Yang Zhang ◽

Hans-Arno Jacobsen ◽

Lulu Tang ◽

Geoffrey Elliott ◽

...

Keyword(s):

Machine Learning ◽

Loss Rate ◽

Differentiated Services ◽

Gradient Boosting ◽

Queuing Delay ◽

End To End Delay ◽

Real Value ◽

Difficult Issue

At present, most publish/subscribe middlewares suppose that there are equal Quality of Service (QoS) requirements for all users. However, in many real-world Internet of Things (IoT) service scenarios, different users may have different delay requirements. How to provide reliable differentiated services has become an urgent problem. The rise of Software-Defined Networking (SDN) provides endless possibilities to improve the QoS of publish/subscribe middlewares due to its greater programmability. We can encode event topics and priorities into flow entries of SDN switches directly to meet customized requirements. In this paper, we first propose an SDN-like publish/subscribe middleware architecture and describe how to use this architecture and priority queues supported by OpenFlow switches to realize differentiated services. Then we present a machine learning method using the eXtreme Gradient Boosting (XGBoost) model to solve the difficult issue of getting the queuing delay of switches accurately. Finally, we propose a reliable differentiated services guarantee mechanism according to the queuing delay and the programmability of SDN to improve QoS, namely, a two-layer queue management mechanism. Experimental evaluations show that the delay predicted by the XGBoost method is closer to the real value; our mechanism can save end-to-end delay, reduce packet loss rate, and allocate bandwidth more reasonably.

An Anomaly Mitigation Framework for IoT Using Fog Computing

Electronics ◽

10.3390/electronics9101565 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1565

Author(s):

Muhammad Aminu Lawal ◽

Riaz Ahmed Shaikh ◽

Syed Raheel Hassan

Keyword(s):

Smart Cities ◽

Fog Computing ◽

Multiclass Classification ◽

Attack Detection ◽

Gradient Boosting ◽

Ip Address ◽

Computing Paradigm ◽

Security Challenges ◽

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster detection when attacks are executed from the blacklisted IP address, while the anomaly-based module uses an extreme gradient boosting algorithm for accurate classification of network traffic flow into normal or abnormal. We evaluated the performance of both modules using an IoT-based dataset in terms response time for the signature-based module and accuracy in binary and multiclass classification for the anomaly-based module. The results show that the signature-based module achieves a fast attack detection of at least six times faster than the anomaly-based module in each number of instances evaluated. The anomaly-based module using the XGBoost classifier detects attacks with an accuracy of 99% and at least 97% for average recall, average precision, and average F1 score for binary and multiclass classification. Additionally, it recorded 0.05 in terms of false-positive rates.

T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm

Frontiers in Microbiology ◽

10.3389/fmicb.2020.580382 ◽

2020 ◽

Vol 11 ◽

Author(s):

Tianhang Chen ◽

Xiangeng Wang ◽

Yanyi Chu ◽

Yanjing Wang ◽

Mingming Jiang ◽

...

Keyword(s):

Gradient Boosting ◽

Type Iv ◽

Proceedings of 2018 Chinese Intelligent Systems Conference - Lecture Notes in Electrical Engineering ◽

An Extreme Gradient Boosting Algorithm for Short-Term Load Forecasting Using Power Grid Big Data

10.1007/978-981-13-2288-4_46 ◽

2018 ◽

pp. 479-490

Author(s):

Liqiang Ren ◽

Limin Zhang ◽

Haipeng Wang ◽

Qiang Guo

Keyword(s):

Big Data ◽

Power Grid ◽

Load Forecasting ◽

Gradient Boosting ◽

Short Term ◽

Short Term Load Forecasting ◽

Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm

IJIIS: International Journal of Informatics and Information Systems ◽

10.47738/ijiis.v3i2.65 ◽

2020 ◽

Vol 3 (2) ◽

pp. 54-59

Author(s):

Marco Febriadi Kokasih ◽

Adi Suryaputra Paramita

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Gradient Boosting ◽

Fair Price ◽

Price Prediction ◽

Online Marketplace ◽

Property Owners ◽

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.

Extreme gradient boosting regression model for soil thermal conductivity

Thermal Science ◽

10.2298/tsci200612001y ◽

2021 ◽

Vol 25 (Spec. issue 1) ◽

pp. 1-7

Author(s):

Ahmet Yurttakal

Keyword(s):

Thermal Conductivity ◽

Soil Moisture ◽

Regression Model ◽

Test Data ◽

Granular Structure ◽

Gradient Boosting ◽

Soil Thermal Conductivity ◽

Complicated Process ◽

The thermal conductivity estimation for the soil is an important step for many geothermal applications. But it is a difficult and complicated process since it involves a variety of factors that have significant effects on the thermal conductivity of soils such as soil moisture and granular structure. In this study, regression was performed with the extreme gradient boosting algorithm to develop a model for estimating thermal conductivity value. The performance of the model was measured on the unseen test data. As a result, the proposed algorithm reached 0.18 RMSE, 0.99 R2, and 3.18% MAE values which state that the algorithm is encouraging.

Research on dairy products detection based on machine learning algorithm

MATEC Web of Conferences ◽

10.1051/matecconf/202235503008 ◽

2022 ◽

Vol 355 ◽

pp. 03008

Author(s):

Yang Zhang ◽

Lei Zhang ◽

Yabin Ma ◽

Jinsen Guan ◽

Zhaoxia Liu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Electronic Nose ◽

Milk Fat ◽

Dairy Products ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

In this study, an electronic nose model composed of seven kinds of metal oxide semiconductor sensors was developed to distinguish the milk source (the dairy farm to which milk belongs), estimate the content of milk fat and protein in milk, to identify the authenticity and evaluate the quality of milk. The developed electronic nose is a low-cost and non-destructive testing equipment. (1) For the identification of milk sources, this paper uses the method of combining the electronic nose odor characteristics of milk and the component characteristics to distinguish different milk sources, and uses Principal Component Analysis (PCA) and Linear Discriminant Analysis , LDA) for dimensionality reduction analysis, and finally use three machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) to build a milk source (cow farm) Identify the model and evaluate and compare the classification effects. The experimental results prove that the classification effect of the SVM-LDA model based on the electronic nose odor characteristics is better than other single feature models, and the accuracy of the test set reaches 91.5%. The RF-LDA and SVM-LDA models based on the fusion feature of the two have the best effect Set accuracy rate is as high as 96%. (2) The three algorithms, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost) and Random Forest (RF), are used to construct the electronic nose odor data for milk fat rate and protein rate. The method of estimating the model, the results show that the RF model has the best estimation performance( R2 =0.9399 for milk fat; R2=0.9301for milk protein). And it prove that the method proposed in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of dairy products.