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Advanced genetic programming techniques for machine learning - A comparative analysis with state-of-the-art automated machine learning methods in the context of imbalanced binary classification
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Orientador(es)
Resumo(s)
The number of available machine learning methods and tools is increasing rapidly, with one recent
trend being the usage of advanced genetic programming concepts and automated machine learning
tools. However, through the rising number of upcoming innovations, it has become a challenge for
machine learning applicants to keep up with all the new opportunities and to identify their potentials.
While emerging methods are typically compared to conventional standard machine learning
algorithms upon their initial introduction, research is still scarce on comparisons of the performances
between the new concepts themselves. Therefore, this thesis provides a comparative analysis of two
novel genetic programming techniques, differentiable Cartesian genetic programming for artificial
neural networks and geometric semantic genetic programming, alongside three state-of-the-art
automated machine learning tools, Auto-Keras, Auto-PyTorch and Auto-sklearn, with regard to their
relative performances in the machine learning subfield of imbalanced binary classification. In this
analysis, the five methods are tested against each other on 20 benchmark datasets, primarily regarding
their average and maximum performance, and subsequently the most successful technique is applied
to the real-world problem of fraud detection. The purpose of this thesis is not only to familiarize
machine learning users with these methods, but above all to determine whether the novel genetic
programming techniques can compete with the more established automated machine learning tools,
and to identify the overall best performing method.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Genetic Programming Automated Machine Learning Imbalanced Binary Classification