An open source resource for AI programming, developed by staff and students at the UTS Australian Artificial Intelligence Institute.

Young female AAII researcher in pink shirt and hair tied back writes on a whiteboard while a female and male researcher are watching in the background

AAII's postdoctoral research fellows, Dr Yiliao Song (front), Dr Qian Zhang (left) and Dr Feng Liu with the Decision Systems and e-Service Intelligence Lab. Photo: Andy Roberts

FuzzyTrees

Development Team: Zhaoqing Liu, Dr Anjin Liu, D/Prof Jie Lu and A/Prof Guangquan Zhang

Get started with FuzzyTrees

FuzzyTrees is a lightweight framework designed for the rapid development of fuzzy decision tree algorithms. The FuzzyTrees framework offers a range of benefits including:

  1. Support in development solutions: FuzzyTrees allows the user to extend new components quickly, according to particular fuzzy decision tree requirements, and build complete algorithm solutions.
  2. Extending components with a set of APIs : Any algorithm can be easily understood by following FuzzyTrees’ uniform APIs. To extend new components with ease, FuzzyTrees provides a set of supporting and easy-to-use utilities, e.g. the splitting and splitting criterion calculation functions available in the most widely used decision tree algorithms, CART, ID3, and C4.5. 
  3. Examples for algorithm development: The FuzzyTrees algorithms, fuzzy CART, fuzzy GBDT and fuzzy RDF can be used as examples for developing new algorithms or for conducting a variety of empirical studies.