MSc Thesis Defense Announcement of Kevin Shi:"An Empirical Analysis of AutoML Tools and Techniques with Automated Feature Engineering"

Friday, September 9, 2022 - 14:30 to 16:00


The School of Computer Science is pleased to present… 

MSc Thesis Defense by: Kevin Shi 

Date: Friday September 9th, 2022 
Time:  2:30pm - 4:00pm 
Passcode: If interested in attending this event, contact the Graduate Secretary at with sufficient notice before the event to obtain the passcode.


Automated machine learning is an approach to automate the creation of machine learning pipelines and models. The ability to automatically create a machine learning pipeline would allow users without machine learning knowledge to create and use machine learning systems. Existing machine learning practitioners can also use these automated approaches to simplify the creation of machine learning systems. As with any tool, effective evaluations of AutoML tools are necessary to ensure users can select the correct tool for their machine learning task. 
In this thesis, we present an empirical analysis of current open-source AutoML tools for tasks within the cybersecurity domain, highlight the current weakness of AutoML tools and evaluate the performance of popular AutoML tools for cybersecurity datasets. In addition, we propose a method of augmenting existing AutoML tools with automated feature engineering and assess the impact of different generation approaches and the effect on total pipeline creation time. 
Keywords: automated machine learning, optimization, genetic algorithm 

MSc Thesis Committee:  

Internal Reader: Dr. Dan Wu       
External Reader: Dr. Ahmed Azab Ismail  
Advisor: Dr. Sherif Saad 
Chair: Dr. Peter Tsin   

MSc Thesis Defense Announcement 

Vector Institute in artificial intelligence, artificial intelligence approved topic logo


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