Efficient Optimization of Hierarchical Multi-Label Classifier Ensembles (Ba/Ma)
Topic for a bachelor/master's thesis
In  and , the authors have demonstrated that a hierarchical decomposition of a multi-label classification problem is beneficial in terms of both efficiency and predictive accuracy. The decomposition in both works is mainly based on relatively simple heuristics. Furthermore, in , an approach for optimizing hierarchical structures was proposed. The aim of this thesis is to extend the approach of  to cope with the relevant structures in  and . Other approaches are also conceivable. Furthermore, the thesis should be centered around the following research questions:
- Is optimizing the hierarchical structure for decomposing the original multi-label classification problem beneficial?
- Is it possible to optimize the hierarchical structures in feasible amount of time?
- Is the choice of base learners an important additional variable for optimization?
Good background in machine learning, especially supervised learning (e.g., classification, regression) and experimental evaluation, algorithm design, programming skills.
-  Tsoumakas, Grigorios, Ioannis Katakis, and Ioannis Vlahavas. "Effective and efficient multilabel classification in domains with large number of labels." Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data (MMD’08). Vol. 21. 2008.
-  Jasinska-Kobus, Kalina, et al. "Probabilistic label trees for extreme multi-label classification." arXiv preprint arXiv:2009.11218 (2020).
-  Wever, Marcel, Felix Mohr, and Eyke Hüllermeier. "Ensembles of evolved nested dichotomies for classification." Proceedings of the Genetic and Evolutionary Computation Conference. 2018.