An open source resource for AI programming, developed by staff and students at the UTS Australian Artificial Intelligence Institute.
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![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](/sites/default/files/styles/wysiwyg_generic_large_x1/public/2021-08/319A1438_AndyRoberts_lr_1200x630_0.jpg?itok=jp2SK8Nx)
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
FuzzyTrees is a lightweight framework designed for the rapid development of fuzzy decision tree algorithms. The FuzzyTrees framework offers a range of benefits including:
- 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.
- 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.
- 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.