Learning comprehensible and accurate hybrid trees

Piltaver, Rok; Luštrek, Mitja; Džeroski, Sašo; Gjoreski, Martin; Gams, Matjaž

doi:10.1016/j.eswa.2020.113980

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…ML algorithms can be adapted to be more transparent. For example, models can output relationships they have found between data features and produce graphics that outline decision processes [271,272]. Ultimately, transparency is key if AI is to be successful combined with 3DP in healthcare settings.…”

Section: Machine Learning Vs Non-ml Techniquesmentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

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Section: Machine Learning Vs Non-ml Techniquesmentioning

confidence: 99%

Harnessing artificial intelligence for the next generation of 3D printed medicines

Elbadawi

McCoubrey

Gavins

et al. 2021

Advanced Drug Delivery Reviews

102

View full text Add to dashboard Cite

show abstract

“…Moreover, on complex classification tasks, as a white-box model, decision trees may be difficult to use sufficiently complex representations to distinguish features of data. To solve this problem, Piltaver et al (2021) have attempted to replace some leaf nodes of decision trees with black-box models, leading to tree classifiers with both interpretable upper layers and accurate lower layers. Further, as the rectifier linear unit (Glorot et al, 2011) in connectionist models preserves both explainability and accuracy, it can be used by the partitioning functions of decision trees (Tao et al, 2020).…”

Section: Related Workmentioning

confidence: 99%