A first approach towards a fuzzy decision tree for multilabel classification

Author(s)

Prati, Ronaldo C. ; Charte, Francisco ; Herrera, Francisco ; IEEE

• Document type: Journal article
• Source: 2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE); v. N/A, p. 6-pg., 2017-01-01.

Abstract

This paper proposes a multilabel fuzzy decision tree classifier named FuzzDT(ML). 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. (AU)

• FAPESP's process: 15/20606-6 - Noise Labels in Machine Learning: Evaluation measures and machine learning algorithms
• Grantee: Ronaldo Cristiano Prati
• Support Opportunities: Scholarships abroad - Research