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

Grant number: 16/14152-5
Support type:Research Grants - Visiting Researcher Grant - International
Duration: October 16, 2016 - October 30, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Márcio Porto Basgalupp
Grantee:Márcio Porto Basgalupp
Visiting researcher: Leander Schietgat
Visiting researcher institution: University of Leuven (KU Leuven), Belgium
Home Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil


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)