Sökning: "Random Survival Forest"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Random Survival Forest.

1. 1. Machine learning for risk ranking of component failure : A comparative study of traditional- and survival machine learning approaches applied to historical data

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Datalogi

   Författare :Fredrik Nilsson; Fanny Fristedt; [2023]
   Nyckelord :Machine learning; Survival analysis; Trains; Freight trains; Damage prediction; Maskininlärning; överlevnadsanalys; tåg; godståg; Skadeprediktion;

   Sammanfattning : This master thesis investigates the use of machine learning for predicting and assessing the risk of railway vehicle component failures. Data used for failure prediction often comes with limitations due to the complex nature of maintenance or sometimes requires investments for the extraction of information. LÄS MER

3. 2. Statistical Modelling of Price Difference Durations Between Limit Order Books: Applications in Smart Order Routing

   Master-uppsats, KTH/Matematisk statistik

   Författare :Hannes Backe; David Rydberg; [2023]
   Nyckelord :Smart Order Routing; Market Microstructure; Statistical Modelling; Survival Analysis; Kaplan-Meier; Cox Proportional Hazards; Random Survival Forest; Smart Order Routing; Marknadsmikrostruktur; Statistisk Modellering; Överlevnadsanalys; Kaplan-Meier; Cox Proportional Hazards; Random Survival Forest;

   Sammanfattning : The modern electronic financial market is composed of a large amount of actors. With the surge in algorithmic trading some of these actors collectively behave in increasingly complex ways. Historically, academic research related to financial markets has been focused on areas such as asset pricing, portfolio management and financial econometrics. LÄS MER

4. 3. Prognostics for Condition Based Maintenance of Electrical Control Units Using On-Board Sensors and Machine Learning

   Master-uppsats, Linköpings universitet/Fordonssystem

   Författare :Gabriel Fredriksson; [2022]
   Nyckelord :machine learning; random forest; random survival forest; condition based maintenance; cbm; reliability; solder joint failure; thermomechanical cycling; ECU; lifetime prediction; data-driven; statistics; BGA; PCBA; field quality; maintenance; truck; bus; vehicle;

   Sammanfattning : In this thesis it has been studied how operational and workshop data can be used to improve the handling of field quality (FQ) issues for electronic units. This was done by analysing how failure rates can be predicted, how failure mechanisms can be detected and how data-based lifetime models could be developed. LÄS MER

5. 4. Application failure predictions from neural networks analyzing telemetry data

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Max Rylander; Filip Hultgren; [2021]
   Nyckelord :;

   Sammanfattning : ith the revolution of the internet, new applications have emerged in our daily life. People are dependent on services for transportation, bank matters, and communication. Services availability is crucial for their survival and competition against other service providers. Achieving good availability is a challenging task. LÄS MER

7. 5. Survival Comparison of Open and Endovascular Repair Using Machine Learning

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Aston Brunnberg; Gustaf Holte; [2021]
   Nyckelord :Master Thesis; Degree Project; Machine Learning; Survival Analysis; Abdominal Aortic Aneurysm; AAA; Kaplan-Meier; Survival Tree; Random Survival Forest; Neural Multi-Task Logistic Regression; DeepHit; Examensarbete; Masteruppsats; Maskininlärning; Överlevnadsanalys; Abdominellt Aortaaneurysm; AAA; Kaplan-Meier; Survival Tree; Random Survival Forest; Neural Multi-Task Logistic Regression; DeepHit;

   Sammanfattning : Today there exists two types of preventive surgical treatment procedures for Abdominal Aortic Aneurysm. In order to make an informed choice of treatment, the clinician needs to have a clear picture of how the choice will affect the patients chances of survival. LÄS MER