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Universal Genetic Programming: a Meta Learning Approach based on Semantics
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Resumo(s)
With the advancements of Machine Learning, the number of predictive models
that can be used in a given situation has grown incredibly, and scientists
willing to use Machine Learning have to spend a significant amount of time
in searching, testing and tuning those models. This has an inevitable impact
on the research quality. Many scientists are currently working on different
approaches to automate this process by devising algorithms that can tune, select
or combine multiple models for a specific application. This is the case
of ensemble methods, hyper-heuristics and meta-learning algorithms. There
have been great progresses in this direction, but typical approaches lack the
presence of an unifying structure onto which these ensemble, hyper or meta
algorithms are developed. In this thesis we discuss about a new meta-learning
method based on Geometric Semantic Genetic Programming. The milestone
introduced by this approach is the use of semantics as an intermediate representation
to work with models of different nature. We will see how this
approach is general and can be applied with any model, in particular we will
apply this case to regression problems and we will test our hypotheses by experimental
verification over some datasets for real-life problems.
Descrição
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Information and Decision Systems
Palavras-chave
Universal Genetic Programming Meta learning Semantics