A first approach towards a fuzzy decision tree for multilabel classification

TitleA first approach towards a fuzzy decision tree for multilabel classification
Publication TypeConference Paper
Year of Publication2017
AuthorsPrati, Ronaldo C., Charte Francisco, and Herrera F.
Conference Name2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Pagination1–6
Date Published7
Conference LocationNaples (Italy)
ISBN Number978-1-5090-6034-4
Abstract

This paper proposes a multilabel fuzzy decision tree classifier named FuzzDTML. The algorithm uses generalized fuzzy entropy, aggregated over all labels, to choose the best attribute for growing the tree. The proposed algorithm also can generate leaves predicting partial label sets, which can incorporate to some degree the dependence among labels, as well as produce more interpretable models. An empirical analysis shows that, although the algorithm does not yet incorporate pruning nor fuzzy interval adjustment phases, it is competitive with other tree based approaches for multilabel classification, with better performance in data sets having numerical features that can be fuzzified.

Notes

TIN2014- 57251-P,P11-TIC-7765

DOI10.1109/FUZZ-IEEE.2017.8015521