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Hyper-heuristics and decision trees for hierarchical multi-label classification problems

Abstract

Hierarchical Multi-label Classification (HMC) is a complex problem, in which classes involved are structured in a hierarchy with hundreds or even thousands of classes. Additionally, instances can be simultaneously classified into more than one path in this hierarchy. These problems are very common, for example, in protein function prediction and annotation of medical images. Among the different algorithms that can be used in these problems, decision tree induction algorithms are a good choice, due their robustness and efficiency, and also because they produce interpretable models with satisfactory performances. However, there are still many open questions about the use of these algorithms in the HMC context, such as which stop and prune criteria to use, which split to use in an internal node, and how to consider the relationships between classes. In addition, only the top-down strategy was used until now. Given such many configuration possibilities, this project aims at implementing a hyper-heuristic for the construction of decision tree induction algorithms, tailored to HMC problems. In contrast to meta-heuristics, hyper-heuristics operate in a higher abstraction level, being used in the search for the best combination of components in the space of possibilities. These components are used to construct the decision tree induction algorithms. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
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VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)